explainerdashboard
This package makes it convenient to quickly deploy a dashboard web app that explains the workings of a (scikit-learn compatible) machine learning model. The dashboard provides interactive plots on model performance, feature importances, feature contributions to individual predictions, "what if" analysis, partial dependence plots, SHAP (interaction) values, visualisation of individual decision trees, etc.
You can also interactively explore components of the dashboard in a notebook/colab environment (or just launch a dashboard straight from there). Or design a dashboard with your own custom layout and explanations (thanks to the modular design of the library).
Installation
You can install the package through pip:
pip install explainerdashboard
Screenshot
Background
In a lot of organizations, especially governmental, but with the GDPR also increasingly in private sector, it is becoming more and more important to be able to explain the inner workings of your machine learning algorithms. Customers have to some extent a right to an explanation why they received a certain prediction, and more and more internal and external regulators require it. With recent innovations in explainable AI (e.g. SHAP values) the old black box trope is no longer valid, but it can still take quite a bit of data wrangling and plot manipulation to get the explanations out of a model. This library aims to make this easy.
The goal is manyfold:
- Make it easy for data scientists to quickly inspect the workings and performance of their model in a few lines of code
- Make it possible for non data scientist stakeholders such as managers, directors, internal and external watchdogs to interactively inspect the inner workings of the model without having to depend on a data scientist to generate every plot and table
- Make it easy to build an application that explains individual predictions of your model for customers that ask for an explanation
- Explain the inner workings of the model to the people working (human-in-the-loop) with it so that they gain understanding what the model does and doesn't do. This is important so that they can gain an intuition for when the model is likely missing information and may have to be overruled.
The library includes:
- Shap values (i.e. what is the contributions of each feature to each individual prediction?)
- Permutation importances (how much does the model metric deteriorate when you shuffle a feature?)
- Partial dependence plots (how does the model prediction change when you vary a single feature?
- Shap interaction values (decompose the shap value into a direct effect an interaction effects)
- For Random Forests and xgboost models: visualisation of individual decision trees
- Plus for classifiers: precision plots, confusion matrix, ROC AUC plot, PR AUC plot, etc
- For regression models: goodness-of-fit plots, residual plots, etc.
The library is designed to be modular so that it should be easy to design your own interactive dashboards with plotly dash, with most of the work of calculating and formatting data, and rendering plots and tables handled by explainerdashboard, so that you can focus on the layout
and project specific textual explanations. (i.e. design it so that it will be interpretable for business users in your organization, not just data scientists)
Alternatively, there is a built-in standard dashboard with pre-built tabs (that you can switch off individually)
Examples of use
Fitting a model, building the explainer object, building the dashboard, and then running it can be as simple as:
ExplainerDashboard(ClassifierExplainer(RandomForestClassifier().fit(X_train, y_train), X_test, y_test)).run()
Below a multi-line example, adding a few extra paramaters.
You can group onehot encoded categorical variables together using the cats
parameter. You can either pass a dict specifying a list of onehot cols per
categorical feature, or if you encode using e.g.
pd.get_dummies(df.Name, prefix=['Name']) (resulting in column names 'Name_Adam', 'Name_Bob')
you can simply pass the prefix 'Name':
from sklearn.ensemble import RandomForestClassifier
from explainerdashboard import ClassifierExplainer, ExplainerDashboard
from explainerdashboard.datasets import titanic_survive, titanic_names
feature_descriptions = {
"Sex": "Gender of passenger",
"Gender": "Gender of passenger",
"Deck": "The deck the passenger had their cabin on",
"PassengerClass": "The class of the ticket: 1st, 2nd or 3rd class",
"Fare": "The amount of money people paid",
"Embarked": "the port where the passenger boarded the Titanic. Either Southampton, Cherbourg or Queenstown",
"Age": "Age of the passenger",
"No_of_siblings_plus_spouses_on_board": "The sum of the number of siblings plus the number of spouses on board",
"No_of_parents_plus_children_on_board" : "The sum of the number of parents plus the number of children on board",
}
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=50, max_depth=5)
model.fit(X_train, y_train)
explainer = ClassifierExplainer(model, X_test, y_test,
cats=['Deck', 'Embarked',
{'Gender': ['Sex_male', 'Sex_female', 'Sex_nan']}],
descriptions=feature_descriptions, # defaults to None
labels=['Not survived', 'Survived'], # defaults to ['0', '1', etc]
idxs = test_names, # defaults to X.index
index_name = "Passenger", # defaults to X.index.name
target = "Survival", # defaults to y.name
)
db = ExplainerDashboard(explainer,
title="Titanic Explainer", # defaults to "Model Explainer"
whatif=False, # you can switch off tabs with bools
)
db.run(port=8050)
From within a notebook
When working inside jupyter or Google Colab you can use
ExplainerDashboard(mode='inline'), ExplainerDashboard(mode='external') or
ExplainerDashboard(mode='jupyterlab'), to run the dashboard inline in the notebook,
or in a seperate tab but keep the notebook interactive.
There is also a specific interface for quickly displaying interactive components
inline in your notebook: InlineExplainer(). For example you can use
InlineExplainer(explainer).shap.dependence() to display the shap dependence
component interactively in your notebook output cell.
Command line tool
You can store explainers to disk with explainer.dump("explainer.joblib")
and then run them from the command-line:
$ explainerdashboard run explainer.joblib
Or store the full configuration of a dashboard to .yaml with e.g.
dashboard.to_yaml("dashboard.yaml") and run it with:
$ explainerdashboard run dashboard.yaml
See explainerdashboard CLI documentation
for details.
Custom dashboards
All the components in the dashboard are modular and re-usable, which means that
you can build your own custom dash dashboards
around them.
By using the built-in ExplainerComponent class it is easy to build your
own layouts, with just a bare minimum of knowledge of html and bootstrap. For
example if you only wanted to display the ShapDependenceComponent, but hide
a few toggles:
from explainerdashboard.custom import *
import dash_bootstrap_components as dbc
import dash_html_components as html
class CustomTab(ExplainerComponent):
def __init__(self, explainer):
super().__init__(explainer, title="Custom Tab")
self.dependence = ShapDependenceComponent(explainer,
hide_selector=True, hide_cats=True, hide_title=True)
self.register_components()
def layout(self):
return dbc.Container([
dbc.Row([
dbc.Col([
html.H3("Shap Dependence Plot:"),
self.dependence.layout()
])
])
])
ExplainerDashboard(explainer, CustomTab).run()
You can use this to define your own layouts, specifically tailored to your
own model, project and needs. See custom dashboard documentation
for more details.
Deployment
If you wish to use e.g. gunicorn to deploy the dashboard you should add
server = db.app.server to your code to expose the Flask server. You can then
start the server with e.g. gunicorn dashboard:server
(assuming the file you defined the dashboard in was called dashboard.py).
See also the ExplainerDashboard section
and the deployment section of the documentation.
