Skip to main content

mercury-explainability

Python 3.10 Python 3.11 Python 3.12 Python 3.13 Apache 2 license Ask Me Anything !

mercury-explainability is a library with implementations of different state-of-the-art methods in the field of explainability. They are designed to work efficiently and to be easily integrated with the main Machine Learning frameworks.

Mercury project at BBVA

Mercury is a collaborative library that was developed by the Advanced Analytics community at BBVA. Originally, it was created as an InnerSource project but after some time, we decided to release certain parts of the project as Open Source. That's the case with the mercury-explainability package.

The basic block of mercury-explainability is the Explainer class. Each one of the explainers in mercury-explainability offers a different method for explaining your models and often will return an Explanation type object containing the result of that particular explainer.

The usage of most of the explainers you will find in this library follows this schema:

from mercury.explainability import ExplainerExample
explainer = ExplainerExample(function_to_explain)
explanation = explainer.explain(dataset)

Basically, you simply need to instantiate your desired Explainer (note that the above example ExplainerExample does not exist) providing your custom function you desire to get an explanation for, which usually will be your model’s inference or evaluation function. These explainers are ready to work efficiently with most of the frameworks you will likely use as a data scientist (yes, included Spark).

If you're interested in learning more about the Mercury project, we recommend reading this blog post from www.bbvaaifactory.com

User installation

The easiest way to install mercury-explainability is using pip:

pip install mercury-explainability

Help and support

This library is currently maintained by a dedicated team of data scientists and machine learning engineers from BBVA.

Documentation

website: https://bbva.github.io/mercury-explainability/site/

Email

mercury.group@bbva.com

Metadata

Release files for mercury-explainability 1.2.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mercury-explainability 1.2.3
File Size Uploaded Provenance Actions
mercury_explainability-1.2.3.tar.gz 3.3 MB Without provenance Details for mercury_explainability-1.2.3.tar.gz

Release files / mercury_explainability-1.2.3.tar.gz

Download URL mercury_explainability-1.2.3.tar.gz
Size 3.3 MB
Tags Source
SHA-256 checksum
How to use checksums
ad00070222265915f25c839891f6943b848dca76327aa56014e3ace87148bad4
BLAKE2b-256 checksum
How to use checksums
e840605d650222703b77db29a270c2b6bc00e9b42f32a8262c0811b06e7193f6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.12

Security for mercury-explainability 1.2.3

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page