TODO.md

TODO

• add get_descriptions_df tests
• do multiclass pdp
• turn print statements into log statements

Bugs:

Plots:

• add hide_legend parameter
• add SHAP decision plots: https://towardsdatascience.com/introducing-shap-decision-plots-52ed3b4a1cba
• make plot background transparent?
• Only use ScatterGl above a certain cutoff
• seperate standard shap plots for shap_interaction plots
  • using some kind of inheritance?
• change lines and annotation to this:
  • https://community.plotly.com/t/announcing-plotly-py-4-12-horizontal-and-vertical-lines-and-rectangles/46783

Classifier plots:

• pdp: add multiclass option
  • no icelines just mean and index with different thickness
  • new method?

Regression plots:

Explainers:

• Turn print statements into logging
• pass n_jobs to pdp_isolate
• add ExtraTrees and GradientBoostingClassifier to tree visualizers
• add plain language explanations
  • could add an parameter to the explainer.plot_* function in_words=True in which case instead of a plot the function returns a verbal description of the relationship in the plot.
  • Then add an "in words" button to the components, that show a popup with the verbal explanation.

notebooks:

Dashboard:

• Turn print statements into logging
• make poweredby right align
• more flexible instantiate_component:
  • no explainer needed (if explainer component detected, pass otherwise ignore)
• add TablePopout
• Add EDA style feature histograms, bar charts, correlation graphs, etc
• add cost calculator/optimizer for classifier models based on confusion matrix weights
  • add Youden J's calculation
• add group fairness metrics
  • https://arxiv.org/pdf/1910.05591.pdf
  • https://cran.r-project.org/web/packages/fairmodels/vignettes/Basic_tutorial.html
  • http://manifold.mlvis.io/
    • generate groups programmatically!

Hub:

• automatic reloads with watchdog
• add reloader=None, debug=None, options
• make example deployment on heroku

Components

• add predictions list to whatif composite:
  • oegedijk#85
• add circular callbacks to cutoff - cutoff percentile
• Add side-by-side option to cutoff selector component
• add index_dropdown=True parameter. Alternative: free entry input.
• add filter to index selector using pattern matching callbacks:
  • https://dash.plotly.com/pattern-matching-callbacks
• add querystring method to ExplainerComponents
• add pos_label_name property to PosLabelConnector search
• add "number of indexes" indicator to RandomIndexComponents for current restrictions
• set equivalent_col when toggling cats in dependence/interactions
• whatif:
  • Add a constraints function to whatif component:
    • tests if current feature input is allowed
    • gives specific feedback when constraint broken
    • could build WhatIfComponentException for this?
  • Add sliders option to what if component

Methods:

• add support for SamplingExplainer, PartitionExplainer, PermutationExplainer, AdditiveExplainer
• add support for LimeTabularExplainer:
  • http://gael-varoquaux.info/interpreting_ml_tuto/content/02_why/04_black_box_interpretation.html
  • https://shap.readthedocs.io/en/latest/generated/shap.explainers.other.LimeTabular.html
• Add this method? : https://arxiv.org/abs/2006.04750?

Tests:

• add get_descriptions_df tests
• add cv metrics tests
• add tests for InterpretML EBM (shap 0.37)
• write tests for explainerhub CLI add user
• test model_output='probability' and 'raw' or 'logodds' seperately
• write tests for explainer_methods
• write tests for explainer_plots

Docs:

• retake screenshots of components as cards
• Add type hints:
  • to explainer class methods
  • to explainer_methods
  • to explainer_plots

Library level:

• Make example heroku deployment repo
• Make example heroku ExplainerHub repo
• submit pull request to shap with broken test for shap/shap#723