Skip to main content

DALEX in Python

Project description

dalex

moDel Agnostic Language for Exploration and eXplanation http://dalex.drwhy.ai/

Overview

Unverified black box model is the path to the failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection.

The dalex package xrays any model and helps to explore and explain its behaviour, helps to understand how complex models are working. The main Explainer object creates a wrapper around a predictive model. Wrapped models may then be explored and compared with a collection of local and global explainers. Recent developents from the area of Interpretable Machine Learning/eXplainable Artificial Intelligence.

The philosophy behind dalex explanations is described in the Explanatory Model Analysis e-book.

The dalex package is a part of DrWhy.AI universe.

Installation

pip install dalex

Resources

Plots

This package uses plotly to render the plots:

Learn more

Machine Learning models are widely used and have various applications in classification or regression tasks. Due to increasing computational power, availability of new data sources and new methods, ML models are more and more complex. Models created with techniques like boosting, bagging of neural networks are true black boxes. It is hard to trace the link between input variables and model outcomes. They are use because of high performance, but lack of interpretability is one of their weakest sides.

In many applications we need to know, understand or prove how input variables are used in the model and what impact do they have on final model prediction. DALEX is a set of tools that help to understand how complex models are working.

Talks about DALEX

Why

76 years ago Isaac Asimov devised Three Laws of Robotics: 1) a robot may not injure a human being, 2) a robot must obey the orders given it by human beings and 3) A robot must protect its own existence. These laws impact discussion around Ethics of AI. Today’s robots, like cleaning robots, robotic pets or autonomous cars are far from being conscious enough to be under Asimov’s ethics.

Today we are surrounded by complex predictive algorithms used for decision making. Machine learning models are used in health care, politics, education, judiciary and many other areas. Black box predictive models have far larger influence on our lives than physical robots. Yet, applications of such models are left unregulated despite many examples of their potential harmfulness. See Weapons of Math Destruction by Cathy O'Neil for an excellent overview of potential problems.

It's clear that we need to control algorithms that may affect us. Such control is in our civic rights. Here we propose three requirements that any predictive model should fulfill.

  • Prediction's justifications. For every prediction of a model one should be able to understand which variables affect the prediction and how strongly. Variable attribution to final prediction.
  • Prediction's speculations. For every prediction of a model one should be able to understand how the model prediction would change if input variables were changed. Hypothesizing about what-if scenarios.
  • Prediction's validations For every prediction of a model one should be able to verify how strong are evidences that confirm this particular prediction.

There are two ways to comply with these requirements. One is to use only models that fulfill these conditions by design. White-box models like linear regression or decision trees. In many cases the price for transparency is lower performance. The other way is to use approximated explainers – techniques that find only approximated answers, but work for any black box model. Here we present such techniques.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dalex-0.1.5.tar.gz (314.7 kB view details)

Uploaded Source

Built Distribution

dalex-0.1.5-py3-none-any.whl (327.5 kB view details)

Uploaded Python 3

File details

Details for the file dalex-0.1.5.tar.gz.

File metadata

  • Download URL: dalex-0.1.5.tar.gz
  • Upload date:
  • Size: 314.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.6

File hashes

Hashes for dalex-0.1.5.tar.gz
Algorithm Hash digest
SHA256 58537784d799fd92fa060d7fdda87b8c8f999528ad2e84debeb6e5b97bfcd93d
MD5 178f681a8fe4e872fdf1101b1b05016a
BLAKE2b-256 6b5d685c3991396523707db3417b4cbee4c834471f86a43983689bc892f38381

See more details on using hashes here.

File details

Details for the file dalex-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: dalex-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 327.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.6

File hashes

Hashes for dalex-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 82ec5cd717c0cbac625fdb466bf2ff1613ccea053094680f9be069489f7c30dc
MD5 fa279ba7deef0851a739d66dc9e5c580
BLAKE2b-256 1f5c5442bf573a019fddfa25528d1ca31de5f0c3232c7a0b18de3ce765c506a1

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page