v0.3.5: skorch support, simplified dashboard and feature descriptions

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!)

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)
• Interactions tab automatically excluded for linear models

v0.3.4.1: fixes detailed shap plots bug when cats=None

bump version

Fixes dtreeviz 1.3 breaking change bug

Release Notes

Version 0.3.4:

Bug Fixes

• Fixes incompatibility bug with dtreeviz >= 1.3
• Fixes ExplainerHub dbc.Jumbotron style bug

Improvements

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

v0.3.3.1: minor bugfix with outliers and nan

Fixes a bug with removing outliers when nan's are present.

v0.3.3: better pipeline support and thread safety

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

v0.3.2.2: more bugfixes

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 passed 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.

v0.3.2.1: add index_dropdown=False to regression dashboard

Bugfix: new index_dropdown=False feature was not working correctly for regression dashboards

v0.3.2: custom metrics

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.

v0.3.1: responsive classifier components

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 component: when only adjusting cutoff, simply updating the cutoff
  line, without recalculating the plot.

v0.3.0.1: dependency fixes

version 0.3.0.1:

Some of the new features of version 0.3 only work with pandas>=1.2, which is not available for python 3.6.

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