RELEASE_NOTES.md

Release Notes

Version 0.3.5:

Breaking Changes

New Features

• adds support for PyTorch Neural Networks! (as long as they are wrapped by skorch)
• adds SimplifiedClassifierComposite and SimplifiedRegressionComposite to explainerdashboard.custom
• adds flag simple=True to load these simplified one page dashboards: ExplainerDashboard(explainer, simple=True)
• adds support for visualizing trees of ExtraTreesClassifier and ExtraTreesRegressor
• adds FeatureDescriptionsComponent to explainerdashboard.custom and the Importances tab
• adds possibility to dynamically add new dashboards to running ExplainerHub using /add_dashboard route with add_dashboard_route=True (will only work if you're running the Hub as a single worker/node though!)

Bug Fixes

Improvements

• ExplainerDashboard.to_yaml("dashboards/dashboard.yaml", dump_explainer=True) will now dump the explainer in the correct subdirectory (and also default to explainer.joblib)

Other Changes

Version 0.3.4:

Bug Fixes

• Fixes incompatibility bug with dtreeviz >= 1.3

Improvements

• raises ValueError when passing shap='deep' as it is not yet correctly supported

Version 0.3.3:

Highlights:

• Adding support for cross validated metrics
• Better support for pipelines by using kernel explainer
• Making explainer threadsafe by adding locks
• Remove outliers from shap dependence plots

Breaking Changes

• parameter permutation_cv has been deprecated and replaced by parameter cv which now also works to calculate cross-validated metrics besides cross-validated permutation importances.

New Features

• metrics now get calculated with cross validation over X when you pass the cv parameter to the explainer, this is useful when for some reason you want to pass the training set to the explainer.
• adds winsorization to shap dependence and shap interaction plots
• If shap='guess' fails (unable to guess the right type of shap explainer), then default to the model agnostic shap='kernel'.
• Better support for sklearn Pipelines: if not able to extract transformer+model, then default to shap.KernelExplainer to explain the entire pipeline
• you can now remove outliers from shap dependence/interaction plots with remove_outliers=True: filters all outliers beyond 1.5*IQR

Bug Fixes

• Sets proper threading.Locks before making calls to shap explainer to prevent race conditions with dashboards calling for shap values in multiple threads. (shap is unfortunately not threadsafe)

Improvements

• single shap row KernelExplainer calculations now go without tqdm progress bar
• added cutoff tpr anf fpr to roc auc plot
• added cutoff precision and recall to pr auc plot
• put a loading spinner on shap contrib table

Other Changes

Version 0.3.2.2:

index_dropdown=False now works for indexes not listed in set_index_list_func() as long as it can be found by set_index_exists_func

New Features

• adds set_index_exists_func to add function that checks for index existing besides those listed by set_index_list_func()

Bug Fixes

• bug fix to make shap.KernelExplainer (used with explainer parametershap='kernel') work with RegressionExplainer
• bug fix when no explicit labels are based with index selector
• component only update if explainer.index_exists(): no IndexNotFoundErrors anymore.
• fixed title for regression index selector labeled 'Custom' bug
• get_y() now returns .item() when necessary
• removed ticks from confusion matrix plot when no labels param passed (this bug got reintroduced in recent plotly release)

Improvements

• new helper function get_shap_row(index) to calculate or look up a single row of shap values.

Version 0.3.2:

Highlights:

• Control what metrics to show or use your own custom metrics using show_metrics
• Set the naming for onehot features with all 0s with cats_notencoded
• Speed up plots by displaying only a random sample of markers in scatter plots with plot_sample.
• make index selection a free text field with index_dropdown=False

New Features

• new parameter show_metrics for both explainer.metrics(), ClassifierModelSummaryComponent and RegressionModelSummaryComponent:
  • pass a list of metrics and only display those metrics in that order
  • you can also pass custom scoring functions as long as they are of the form metric_func(y_true, y_pred): show_metrics=[metric_func]
    • For ClassifierExplainer what is passed to the custom metric function depends on whether the function takes additional parameters cutoff and pos_label. If these are not arguments, then y_true=self.y_binary(pos_label) and y_pred=np.where(self.pred_probas(pos_label)>cutoff, 1, 0). Else the raw self.y and self.pred_probas are passed for the custom metric function to do something with.
    • custom functions are also stored to dashboard.yaml and imported upon loading ExplainerDashboard.from_config()
• new parameter cats_notencoded: a dict to indicate how to name the value of a onehotencoded features when all onehot columns equal 0. Defaults to 'NOT_ENCODED', but can be adjusted with this parameter. E.g. cats_notencoded=dict(Deck="Deck not known").
• new parameter plot_sample to only plot a random sample in the various scatter plots. When you have a large dataset, this may significantly speed up various plots without sacrificing much in expressiveness: ExplainerDashboard(explainer, plot_sample=1000).run
• new parameter index_dropdown=False will replace the index dropdowns with a free text field. This can be useful when you have a lot of potential indexes, and the user is expected to know the index string. Input will be checked for validity with explainer.index_exists(index), and field indicates when input index does not exist. If index does not exist, will not be forwarded to other components, unless you also set index_check=False.
• adds mean absolute percentage error to the regression metrics. If it is too large a warning will be printed. Can be excluded with the new show_metrics parameter.

Bug Fixes

• get_classification_df added to ClassificationComponent dependencies.

Improvements

• accepting single column pd.Dataframe for y, and automatically converting it to a pd.Series
• if WhatIf FeatureInputComponent detects the presence of missing onehot features (i.e. rows where all columns of the onehotencoded feature equal 0), then adds 'NOT_ENCODED' or the matching value from cats_notencoded to the dropdown options.
• Generating name for parameters for ExplainerComponents for which no name is given is now done with a determinative process instead of a random uuid. This should help with scaling custom dashboards across cluster deployments. Also drops shortuuid dependency.
• ExplainerDashboard now prints out local ip address when starting dashboard.
• get_index_list() is only called once upon starting dashboard.

Other Changes

Version 0.3.1:

This version is mostly about pre-calculating and optimizing the classifier statistics components. Those components should now be much more responsive with large datasets.

New Features

• new methods roc_auc_curve(pos_label) and pr_auc_curve(pos_label)
• new method get_classification_df(...) to get dataframe with number of labels above and below a given cutoff.
  • this now gets used by plot_classification(..)
• new method confusion_matrix(cutoff, binary, pos_label)
• added parameters sort_features to FeatureInputComponent:
  • defaults to 'shap': order features by mean absolute shap
  • if set to 'alphabet' features are sorted alphabetically
• added parameter fill_row_first to FeatureInputComponent:
  • defaults to True: fill first row first, then next row, etc
  • if False: fill first column first, then second column, etc

Bug Fixes

• categorical mappings now updateable with pandas<=1.2 and python==3.6
• title now overridable for RegressionRandomIndexComponent
• added assert check on summary_type for ShapSummaryComponent

Improvements

• pre-Calculating lift_curve_df only once and then storing for each pos_label
  • plus: storing only 100 evenly spaced rows of lift_curve_df
  • dashboard should be more responsive for large datasets
• pre-calculating roc_auc_curve and pr_auc_curve
  • dashboard should be more responsive for large datasets
• pre-calculating confusion matrices
  • dashboard should be more responsive for large datasets
• pre-calculating classification_dfs
  • dashboard should be more responsive for large datasets
• confusion matrix: added axis title, moved predicted labels to bottom of graph
• precision plot: when only adjusting cutoff, simply updating the cutoff line, without recalculating the plot.

Other Changes

version 0.3.0.1:

Breaking Changes

• new dependency requirements pandas>=1.2 also implies python>=3.7

Bug Fixes

• updates pandas version to be compatible with categorical feature operations
• updates dtreeviz version to make xgboost and pyspark dependencies optional

Improvements

Other Changes

version 0.3.0:

This is a major release and comes with lots of breaking changes to the lower level ClassifierExplainer and RegressionExplainer API. The higherlevel ExplainerComponent and ExplainerDashboard API has not been changed however, except for the deprecation of the cats and hide_cats parameters.

Explainers generated with version explainerdashboard <= 0.2.20.1 will not work with this version, so if you have stored explainers to disk you either have to rebuild them with this new version, or downgrade back to explainerdashboard==0.2.20.1! (hope you pinned your dependencies in production! ;)

Main motivation for these breaking changes was to improve memory usage of the dashboards, especially in production. This lead to the deprecation of the dual cats grouped/not grouped functionality of the dashboard. Once I had committed to that breaking change, I decided to clean up the entire API and do all the needed breaking changes at once.

Breaking Changes

• onehot encoded features are now merged by default. This means that the cats=True parameter has been removed from all explainer methods, and the group cats toggle has been removed from all ExplainerComponents. This saves both on code complexity and memory usage. If you wish to see the see the individual contributions of onehot encoded columns, simply don't pass them to the cats parameter upon construction.

• Deprecated explainer attributes:

  • BaseExplainer:
    • self.shap_values_cats
    • self.shap_interaction_values_cats
    • permutation_importances_cats
    • self.get_dfs()
    • formatted_contrib_df()
    • self.to_sql()
    • self.check_cats()
    • equivalent_col
  • ClassifierExplainer:
    • get_prop_for_label

• Naming changes to attributes:

  • BaseExplainer:
    • importances_df() -> get_importances_df()
    • feature_permutations_df() -> get_feature_permutations_df()
    • get_int_idx(index) -> get_idx(index)
    • importances_df() -> get_importances_df()
    • contrib_df() -> get_contrib_df() *
    • contrib_summary_df() -> self.get_summary_contrib_df() *
    • interaction_df() -> get_interactions_df() *
    • shap_values -> get_shap_values_df
    • plot_shap_contributions() -> plot_contributions()
    • plot_shap_summary() -> plot_importances_detailed()
    • plot_shap_dependence() -> plot_dependence()
    • plot_shap_interaction() -> plot_interaction()
    • plot_shap_interaction_summary() -> plot_interactions_detailed()
    • plot_interactions() -> plot_interactions_importance()
    • n_features() -> n_features
    • shap_top_interaction() -> top_shap_interactions
    • shap_interaction_values_by_col() -> shap_interactions_values_for_col()
  • ClassifierExplainer:
    • self.pred_probas -> self.pred_probas()
    • precision_df() -> get_precision_df() *
    • lift_curve_df() -> get_liftcurve_df() *
  • RandomForestExplainer/XGBExplainer:
    • decision_trees -> shadow_trees
    • decisiontree_df() -> get_decisionpath_df()
    • decisiontree_summary_df() -> get_decisionpath_summary_df()
    • decision_path_file() -> decisiontree_file()
    • decision_path() -> decisiontree()
    • decision_path_encoded() -> decisiontree_encoded()

New Features

• new Explainer parameter precision: defaults to 'float64'. Can be set to 'float32' to save on memory usage: ClassifierExplainer(model, X, y, precision='float32')
• new memory_usage() method to show which internal attributes take the most memory.
• for multiclass classifiers: keep_shap_pos_label_only(pos_label) method:
  • drops shap values and shap interactions for all labels except pos_label
  • this should significantly reduce memory usage for multi class classification models.
  • not needed for binary classifiers.
• added get_index_list(), get_X_row(index), and get_y(index) methods.
  • these can be overridden with .set_index_list_func(), .set_X_row_func() and .set_y_func().
  • by overriding these functions you can for example sample observations from a database or other external storage instead of from X_test, y_test.
• added Popout buttons to all the major graphs that open a large modal showing just the graph. This makes it easier to focus on a particular graph without distraction from the rest of the dashboard and all it's toggles.
• added max_cat_colors parameters to plot_importance_detailed and plot_dependence and plot_interactions_detailed
  • prevents plotting getting slow with categorical features with many categories.
  • defaults to 5
  • can be set as **kwarg to ExplainerDashboard
• adds category limits and sorting to RegressionVsCol component
• adds property X_merged that gives a dataframe with the onehot columns merged.

Bug Fixes

• shap dependence: when no point cloud, do not highlight!
• Fixed bug with calculating contributions plot/table for whatif component, when InputFeatures had not fully loaded, resulting in shap error.

Improvements

• saving X.copy(), instead of using a reference to X
  • this would result in more memory usage in development though, so you can del X_test to save memory.
• ClassifierExplainer only stores shap (interaction) values for the positive class: shap values for the negative class are generated on the fly by multiplying with -1.
• encoding onehot columns as np.int8 saving memory usage
• encoding categorical features as pd.category saving memory usage
• added base TreeExplainer class that RandomForestExplainer and XGBExplainer both derive from
  • will make it easier to extend tree explainers to other models in the future
    • e.g. catboost and lightgbm
• got rid of the callable properties (that were their to assure backward compatibility), and replaced them with regular methods.

Other Changes

0.2.20.1:

Bug Fixes

• fixes bug allowing single list of logins for ExplainerDashboard when passed on to ExplainerHub
• fixes bug with explainer generated with explainerdashboard < version 0.2.20 that did not have a onehot_cols property

0.2.20:

Breaking Changes

• WhatIfComponent deprecated. Use WhatIfComposite or connect components yourself to a FeatureInputComponent
• renaming properties: explainer.cats -> explainer.onehot_cols explainer.cats_dict -> explainer.onehot_dict

New Features

• Adds support for model with categorical features that were not onehot encoded (e.g. CatBoost)
• Adds filter on number of categories to display in violin plots and pdp plot, and how to sort the categories (alphabetical, by frequency or by mean abs shap)

Bug Fixes

• fixes bug where str tab indicators returned e.g. the old ImportancesTab instead of ImportancesComposite

Improvements

• No longer dependening on PDPbox dependency: built own partial dependence functions with categorical feature support
• autodetect xgboost.core.Booster or lightgbm.Booster and give ValueError to use the sklearn compatible wrappers instead.

Other Changes

• Introduces list of categorical columns: explainer.categorical_cols
• Introduces dictionary with categorical columns categories: explainer.categorical_dict
• Introduces list of all categorical features: explainer.cat_cols

0.2.19

Breaking Changes

• ExplainerHub: parameter user_json is now called users_file (and default to a users.yaml file)
• Renamed a bunch of ExplainerHub private methods:
  • _validate_user_json -> _validate_users_file
  • _add_user_to_json -> _add_user_to_file
  • _add_user_to_dashboard_json -> _add_user_to_dashboard_file
  • _delete_user_from_json -> _delete_user_from_file
  • _delete_user_from_dashboard_json -> _delete_user_from_dashboard_file

New Features

• Added NavBar to ExplainerHub
• Made users.yaml to default file for storing users and hashed passwords for ExplainerHub for easier manual editing.
• Added option min_height to ExplainerHub to set the size of the iFrame containing the dashboard.
• Added option fluid=True to ExplainerHub to stretch bootstrap container to width of the browser.
• added parameter bootstrap to ExplainerHub to override default bootstrap theme.
• added option dbs_open_by_default=True to ExplainerHub so that no login is required for dashboards for which there wasn't a specific lists of users declared through db_users. So only dashboards for which users have been defined are password protected.
• Added option no_index to ExplainerHub so that no flask route is created for index "/", so that you can add your own custom index. The dashboards are still loaded on their respective routes, so you can link to them or embed them in iframes, etc.
• Added a "wizard" perfect prediction to the lift curve.
  • hide with hide_wizard=True default to not show with wizard=False.

Bug Fixes

• ExplainerHub.from_config() now works with non-cwd paths
• ExplainerHub.to_yaml("subdirectory/hub.yaml") now correctly stores the users.yaml file in the correct subdirectory when specified.

Improvements

• added a "powered by: explainerdashboard" footer. Hide it with hide_poweredby=True.
• added option "None" to shap dependence color col. Also removes the point cloud from the violin plots for categorical features.
• added option mode to ExplainerDashboard.run() that can override self.mode.

Other Changes

0.2.18.1:

Breaking Changes

New Features

• ExplainerHub now does user managment through Flask-Login and a user.json file
• adds an explainerhub cli to start explainerhubs and do user management.

Bug Fixes

Improvements

Other Changes

0.2.17:

Breaking Changes

New Features

• Introducing ExplainerHub: combine multiple dashboards together behind a single frontend with convenient url paths.
  • example:

  db1 = ExplainerDashboard(explainer, title="Dashboard One", name='dashboard1')
  db2 = ExplainerDashboard(explainer, title="Dashboard Two", name='dashboard2')
  
  hub = ExplainerHub([db1, db2])
  hub.run()
  
  # store an recover from config:
  hub.to_yaml("hub.yaml")
  hub2 = ExplainerHub.from_config("hub.yaml")

• adds option dump_explainer to ExplainerDashboard.to_yaml to automatically dump the explainerfile along with the yaml.
• adds option use_waitress to ExplainerDashboard.run() and ExplainerHub.run(), to use the waitress python webserver instead of the Flask development server
• adds parameters to ExplainerDashboard:
  • name: this will be used to assign a url for ExplainerHub
  • description: this will be used for the title tooltip in the dashboard and in the ExplainerHub frontend.

Bug Fixes

Improvements

• the cli now uses the waitress server by default.

Other Changes

Version 0.2.16.2:

Bug fix/Improvement

• Makes component name property for the default composites deterministic instead of random uuid, now also working when loading a dashboard .from_config()
  • note however that for custom ExplainerComponents the user is still responsible for making sure that all subcomponents get assigned a deterministic name (otherwise random uuid names get assigned at dashboard start, which might differ across nodes in e.g. docker swarm deployments)
• Calling self.register_components() no longer necessary.

Version 0.2.16.1:

Bug fix/Improvement

• Makes component name property for the default composites deterministic instead of random uuid. This should help remedy bugs with deployment using e.g. docker swarm.
  • When you pass a list of ExplainerComponents to ExplainerDashboard the tabs will get names '1', '2', '3', etc.
  • If you then make sure that subcomponents get passed a name like name=self.name+"1", then subcomponents will have deterministic names as well.
  • this has been implemented for the default Composites that make up the default explainerdashboard

Version 0.2.16:

Breaking Changes

• hide_whatifcontribution parameter now called hide_whatifcontributiongraph

New Features

• added parameter n_input_cols to FeatureInputComponent to select in how many columns to split the inputs
• Made PredictionSummaryComponent and ShapContributionTableComponent also work with InputFeatureComponent
• added a PredictionSummaryuComponent and ShapContributionTableComponent to the "what if" tab

Bug Fixes

Improvements

• features of FeatureInputComponent are now ordered by mean shap importance
• Added range indicator for numerical features in FeatureInputComponent
  • hide them hide_range=True
• changed a number of dropdowns from dcc.Dropdown to dbc.Select
• reordered the regression random index selector component a bit

Other Changes

Version 0.2.15:

Breaking Changes

New Features

• can now hide entire components on tabs/composites:

  ExplainerDashboard(explainer, 
      # importances tab:
      hide_importances=True,
      # classification stats tab:
      hide_globalcutoff=True, hide_modelsummary=True, 
      hide_confusionmatrix=True, hide_precision=True, 
      hide_classification=True, hide_rocauc=True, 
      hide_prauc=True, hide_liftcurve=True, hide_cumprecision=True,
      # regression stats tab:
      # hide_modelsummary=True, 
      hide_predsvsactual=True, hide_residuals=True, 
      hide_regvscol=True,
      # individual predictions:
      hide_predindexselector=True, hide_predictionsummary=True,
      hide_contributiongraph=True, hide_pdp=True, 
      hide_contributiontable=True,
      # whatif:
      hide_whatifindexselector=True, hide_inputeditor=True, 
      hide_whatifcontribution=True, hide_whatifpdp=True,
      # shap dependence:
      hide_shapsummary=True, hide_shapdependence=True,
      # shap interactions:
      hide_interactionsummary=True, hide_interactiondependence=True,
      # decisiontrees:
      hide_treeindexselector=True, hide_treesgraph=True, 
      hide_treepathtable=True, hide_treepathgraph=True,
      ).run()
  

Bug Fixes

• Fixed bug where if you passed a default index as **kwarg, the random index selector would still fire at startup, overriding the passed index
• Fixed bug where in case of ties in shap values the contributions graph/table would show more than depth/topx feature
• Fixed bug where favicon was not showing when using custom bootstrap theme
• Fixed bug where logodds where multiplied by 100 in ShapContributionTableComponent

Improvements

• added checks on logins parameter to give more helpful error messages
  • also now accepts a single pair of logins: logins=['user1', 'password1']
• added a hide_footer parameter to components with a CardFooter

Other Changes

Version 0.2.14:

Breaking Changes

New Features

• added bootstrap parameter to dashboard to make theming easier: e.g. ExplainerDashboard(explainer, bootstrap=dbc.themes.FLATLY).run()
• added hide_subtitle=False parameter to all components with subtitles
• added description parameter to all components to adjust the hover-over-title tooltip
• can pass additional *kwargs to ExplainerDashboard.from_config() to override stored parameters, e.g. db = ExplainerDashboard.from_config("dashboard.yaml", higher_is_better=False)

Bug Fixes

• fixed bug where drop_na=True for explainer.plot_pdp() was not working.

Improvements

• **kwargs are now also stored when calling ExplainerDashboard.to_yaml()
• turned single radioitems into switches
• RegressionVsColComponent: hide "show point cloud next to violin" switch when feature is not in cats

Other Changes

Version 0.2.13.2

Bug Fixes

• fixed RegressionRandomIndexComponent bug that crashed when y.astype(np.int64), now casting all slider ranges to float.

Version 0.2.13.1

Bug Fixes

• fixed pdp bug introduced with setting X.index to self.idxs where the highlighted index was not the right index
• now hiding entire CardHeader when hide_title=True
• index was not initialized in ShapContributionsGraphComponent and Shap ContributionsTableComponent

Version 0.2.13:

Breaking Changes

• Now always have to pass a specific port when terminating a JupyterDash-based (i.e. inline, external or jupyterlab) dashboard: ExplainerDashboard.terminate(port=8050)
  • but now also works as a classmethod, so don't have to instantiate an actual dashboard just to terminate one!
• ExplainerComponent _register_components has been renamed to component_callbacks to avoid the confusing underscore

New Features

• new: ClassifierPredictionSummaryComponent,RegressionPredictionSummaryComponent
  • already integrated into the individual predictions tab
  • also added a piechart with predictions
• Wrapped all the ExplainerComponents in dbc.Card for a cleaner look to the dashboard.
• added subtitles to all components

Bug Fixes

Improvements

• using go.Scattergl instead of go.Scatter for some plots which should improve performance with larger datasets
• ExplainerDashboard.terminate() is now a classmethod, so don't have to build an ExplainerDashboard instance in order to terminate a running JupyterDash dashboard.
• added disable_permutations boolean argument to ImportancesComponent (that you can also pass to ExplainerDashboard **kwargs)

Other Changes

• Added warning that kwargs get passed down the ExplainerComponents
• Added exception when trying to use ClassifierRandomIndexComponent with a RegressionExplainer or RegressionRandomIndexComponent with a ClassifierExplainer
• dashboard now uses Composites directly instead of the ExplainerTabs

Version 0.2.12:

Breaking Changes

• removed metrics_markdown() method. Added metrics_descriptions() that describes the metric in words.
• removed PredsVsColComponent, ResidualsVsColComponent and ActualVsColComponent, these three are now subsumed in RegressionVsColComponent.

New Features

• Added tooltips everywhere throughout the dashboard to explainer components, plots, dropdowns and toggles of the dashboard itself.

Bug Fixes

Improvements

• changed colors on contributions graph up=green, down=red
  • added higher_is_better parameter to switch green and red colors.
• Clarified wording on index selector components
• hiding group cats toggle everywhere when no cats are passed
• passing **kwargs of ExplainerDashbaord down to all all tabs and (sub) components so that you can configure components from an ExplainerDashboard param. e.g. ExplainerDashboard(explainer, higher_is_better=False).run() will pass the higher_is_better param down to all components. In the case of the ShapContributionsGraphComponent and the XGBoostDecisionTrees component this will cause the red and green colors to flip (normally green is up and red is down.)

Other Changes

Version 0.2.11:

Breaking Changes

New Features

• added (very limited) sklearn.Pipeline support. You can pass a Pipeline as model parameter as long as the pipeline either:
  1. Does not add, remove or reorders any input columns
  2. has a .get_feature_names() method that returns the new column names (this is currently beings debated in sklearn SLEP007)
• added cutoff slider to CumulativePrecisionComponent
• For RegressionExplainer added ActualVsColComponent and PredsVsColComponent in order to investigate partial correlations between y/preds and various features.
• added index_name parameter: name of the index column (defaults to X.index.name or idxs.name). So when you pass index_name="Passenger", you get a "Random Passenger" button on the index selector instead of "Random Index", etc.

Bug Fixes

• Fixed a number of bugs for when no labels are passed (y=None):
  • fixing explainer.random_index() for when y is missing
  • Hiding label/y/residuals selector in RandomIndexSelectors
  • Hiding y/residuals in prediction summary
  • Hiding model_summary tab
  • Removing permutation importances from dashboard

Improvements

• Seperated labels for "observed" and "average prediction" better in tree plot
• Renamed "actual" to "observed" in prediction summary
• added unique column check for whatif-component with clearer error message
• model metrics now formatted in a nice table
• removed most of the loading spinners as most graphs are not long loads anyway.

Other Changes

Version 0.2.10:

New Features

• Explainer parameter cats now takes dicts as well where you can specify your own groups of onehotencoded columns. - e.g. instead of passing cats=['Sex'] to group ['Sex_female', 'Sex_male', 'Sex_nan'] you can now do this explicitly: cats={'Gender'=['Sex_female', 'Sex_male', 'Sex_nan']} - Or combine the two methods: cats=[{'Gender'=['Sex_female', 'Sex_male', 'Sex_nan']}, 'Deck', 'Embarked']

Version 0.2.9:

Breaking Changes

New Features

• You don't have to pass the list of subcomponents in self.register_components() anymore: it will infer them automatically from self.__dict__.

Improvements

• ExplainerComponents now automatically stores all parameters to attributes
• ExplainerComponents now automatically stores all parameters to a ._stored_params dict
• ExplainerDashboard.to_yaml() now support instantiated tabs and stores parameters to yaml
• ExplainerDashboard.to_yaml() now stores the import requirments of subcomponents
• ExplainerDashboard.from_config() now instantiates tabs with stored parameters
• ExplainerDashboard.from_config() now imports classes of subcomponents

Other Changes

• added docstrings to explainer_plots
• added screenshots of ExplainerComponents to docs
• added more gifs to the documentation

Version 0.2.8:

Breaking Changes

• split explainerdashboard.yaml into a explainer.yaml and dashboard.yaml
• Changed UI of the explainerdashboard CLI to reflect this
• This will make it easier in the future to have automatic rebuilds and redeploys when an modelfile, datafile or configuration file changes.

New Features

• Load an ExplainerDashboard from a configuration file with the classmethod, e.g. : ExplainerDashboard.from_config("dashboard.yaml")

Bug Fixes

Improvements

Other Changes

Version 0.2.7:

Breaking Changes

New Features

• explainer.dump() to store explainer, explainer.from_file() to load explainer from file
• Explainer.to_yaml() and ExplainerDashboard.to_yaml() can store the configuration of your explainer/dashboard to file.
• explainerdashboard CLI:
  • Start an explainerdashboard from the command-line!
  • start default dashboard from stored explainer : explainerdashboard run explainer.joblib
  • start full configured dashboard from config: explainerdashboard run explainerdashboard.yaml
  • build explainer based on input files defined in .yaml (model.pkl, data.csv, etc): explainerdashboard build explainerdashboard.yaml
  • includes new ascii logo :)

Bug Fixes

Improvements

• If idxs is not passed use X.index instead
• explainer.idxs performance enhancements
• added whatif component and tab to InlineExplainer
• added cumulative precision component to InlineExplainer

Other Changes

Version 0.2.6:

Improvements

• more straightforward imports: from explainerdashboard import ClassifierExplainer, RegressionExplainer, ExplainerDashboard, InlineExplainer
• all custom imports (such as ExplainerComponents, Composites, Tabs, etc) combined under explainerdashboard.custom: from explainerdashboard.custom import *

version 0.2.5:

Breaking Changes

New Features

• New dashboard tab: WhatIfComponent/WhatIfComposite/WhatIfTab: allows you to explore whatif scenario's by editing multiple featues and observing shap contributions and pdp plots. Switch off with ExplainerDashboard parameter whatif=False.
• New login functionality: you can restrict access to your dashboard by passing a list of [login, password] pairs: ExplainerDashboard(explainer, logins=[['login1', 'password1'], ['login2', 'password2']]).run()
• Added 'target' parameter to explainer, to make more descriptive plots. e.g. by setting target='Fare', will show 'Predicted Fare' instead of simply 'Prediction' in various plots.
• in detailed shap/interaction summary plots, can now click on single shap value for a particular feature, and have that index highlighted for all features.
• autodetecting Google colab environment and setting mode='external' (and suggesting so for jupyter notebook environments)
• confusion matrix now showing both percentage and counts
• Added classifier model performance summary component
• Added cumulative precision component

Bug Fixes

Improvements

• added documentation on how to deploy to heroku
• Cleaned up modebars for figures
• ClassifierExplainer asserts predict_proba attribute of model
• with model_output='logodds' still display probability in prediction summary
• for ClassifierExplainer: check if has predict_proba methods at init

Other Changes

• removed monkeypatching shap_explainer note

version 0.2.4

New Features

• added ExplainerDashboard parameter "responsive" (defaults to True) to make the dashboard layout reponsive on mobile devices. Set it to False when e.g. running tests on headless browsers.

Bug Fixes

• Fixes bug that made RandomForest and xgboost explainers unpicklable

Improvements

• Added tests for picklability of explainers

Version 0.2.3

Breaking Changes

• RandomForestClassifierExplainer and RandomForestRegressionExplainer will be deprecated: can now simply use ClassifierExplainer or RegressionExplainer and the mixin class will automatically be loaded.

New Features

• Now also support for visualizing individual trees for XGBoost models! (XGBClassifier and XGBRegressor). The XGBExplainer mixin class will be automatically loaded and make decisiontree_df(), decision_path() and plot_trees() methods available, Decision Trees tab and components now also work for XGBoost models.
• new parameter n_jobs for calculations that can be parallelized (e.g. permutation importances)
• contrib_df, plot_shap_contributions: can order by global shap feature importance with sort='importance' (as well as 'abs', 'high-to-low' 'low-to-high')
• added actual outcome to plot_trees (for both RandomForest and XGB)

Bug Fixes

Improvements

• optimized code for calculating permutation importance, adding possibility to calculate in parallel
• shap dependence component: if no color col selected, output standard blue dots instead of ignoring update

Other Changes

• added selenium integration tests for dashboards (also working with github actions)
• added tests for multiclass classsification, DecisionTree and ExtraTrees models
• added tests for XGBExplainers
• added proper docstrings to explainer_methods.py

Version 0.2.2

Bug Fixes

• kernel shap bug fixed
• contrib_df bug with topx fixed
• fix for shap v0.36: import approximate_interactions from shap.utils instead of shap.common

Version 0.2.1:

Breaking Changes

• Removed ExplainerHeader from ExplainerComponents
  • so also removed parameter header_mode from ExplainerComponent parameters
  • You can now instead syncronize pos labels across components with a PosLabelSelector and PosLabelConnector.
• In regression plots instead of boolean ratio=True/False, you now pass residuals={'difference', 'ratio', 'log-ratio'}
• decisiontree_df_summary renamed to decisiontree_summary_df (in line with contrib_summary_df)

New Features

• added check all shap values >-1 and <1 for model_output=probability
• added parameter pos_label to all components and ExplainerDashboard to set the initial pos label
• added parameter block_selector_callbacks to ExplainerDashboard to block the global pos label selector's callbacks. If you already have PosLabelSelectors in your layout, this prevents clashes.
• plot actual vs predicted now supported only logging x axis or only y axis
• residuals plots now support option residuals='log-ratio'
• residuals-vs-col plot now shows violin plot for categorical features
• added sorting option to contributions plot/graph: sort={'abs', 'high-to-low', 'low-to-high'}
• added final prediction to contributions plot

Bug Fixes

• Interaction connector bug fixed in detailed summary: click didn't work
• pos label was ignored in explainer.plot_pdp()
• Fixed some UX issues with interations components

Improvements

• All State['tabs', 'value'] condition have been taken out of callbacks. This used to fix some bugs with dash tabs, but seems it works even without, so also no need to insert dummy_tabs in ExplainerHeader.
• All ExplainerComponents now have their own pos label selector, meaning that they are now fully self-containted and independent. No global dash elements in component callbacks.
• You can define the layout of ExplainerComponents in a layout() method instead of _layout(). Should still define component_callbacks() to define callbacks so that all subcomponents that have been registered will automatically get their callbacks registered as well.
• Added regression self.units to prediction summary, shap plots, contributions plots/table, pdp plot and trees plot.
• Clearer title for MEAN_ABS_SHAP importance and summary plots
• replace na_fill value in contributions table by "MISSING"
• add string idxs to shap and interactions summary and dependence plots, including the violing plots
• pdp plot for classification now showing percentages instead of fractions

Other Changes

• added hide_title parameter to all components with a title
• DecisionPathGraphComponent not available for RandomForestRegression models for now.
• In contributions graph base value now called 'population average' and colored yellow.

version 0.2:

Breaking Changes

• InlineExplainer api has been completely redefined
• JupyterExplainerDashboard, ExplainerTab and JupyterExplainerTab have been deprecated

New Features

• Major rewrite and refactor of the dashboard code, now modularized into ExplainerComponents and ExplainerComposites.
• ExplainerComponents can now be individually accessed through InlineExplainer
• All elements of components can now be switched on or off or be given an initial value.
• Makes it much, much easier to design own custom dashboards.
• ExplainerDashboard can be passed an arbitrary list of components to display as tabs.

Better docs:

• Added sections InlineExplainer, ExplainerTabs, ExplainerComponents, CustomDashboards and Deployment
• Added screenshots to documentation.

Bug Fixes

• fixes residuals y-pred instead of pred-y

Improvements

• Random Index Selector redesigned
• Prediction summary redesigned
• Tables now follow dbc.Table formatting
• All connections between components now happen through explicit connectors
• Layout of most components redesigned, with all elements made hideable

Other Changes

Version 0.1.13

Bug Fixes

• Fixed bug with GradientBoostingClassifier where output format of shap.expected_value was not not properly accounted for.

Improvements

• Cleaned up standalone label selector code
• Added check for shap base values to be between between 0 and 1 for model_output=='probability'

Version 0.1.12

Breaking Changes

• ExplainerDashboardStandaloneTab is now called ExplainerTab

New Features

added support for the jupyter-dash package for inline dashboard in Jupyter notebooks, adding the following dashboard classes:

• JupyterExplainerDashboard
• JupyterExplainerTab
• InlineExplainer

Template:

Breaking Changes

New Features

Bug Fixes

Improvements

Other Changes