A library for debugging/inspecting machine learning classifiers and explaining their predictions

Overview

ELI5

PyPI Version Build Status Code Coverage Documentation

ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions.

explain_prediction for text data

explain_prediction for image data

It provides support for the following machine learning frameworks and packages:

  • scikit-learn. Currently ELI5 allows to explain weights and predictions of scikit-learn linear classifiers and regressors, print decision trees as text or as SVG, show feature importances and explain predictions of decision trees and tree-based ensembles. ELI5 understands text processing utilities from scikit-learn and can highlight text data accordingly. Pipeline and FeatureUnion are supported. It also allows to debug scikit-learn pipelines which contain HashingVectorizer, by undoing hashing.
  • Keras - explain predictions of image classifiers via Grad-CAM visualizations.
  • xgboost - show feature importances and explain predictions of XGBClassifier, XGBRegressor and xgboost.Booster.
  • LightGBM - show feature importances and explain predictions of LGBMClassifier and LGBMRegressor.
  • CatBoost - show feature importances of CatBoostClassifier, CatBoostRegressor and catboost.CatBoost.
  • lightning - explain weights and predictions of lightning classifiers and regressors.
  • sklearn-crfsuite. ELI5 allows to check weights of sklearn_crfsuite.CRF models.

ELI5 also implements several algorithms for inspecting black-box models (see Inspecting Black-Box Estimators):

  • TextExplainer allows to explain predictions of any text classifier using LIME algorithm (Ribeiro et al., 2016). There are utilities for using LIME with non-text data and arbitrary black-box classifiers as well, but this feature is currently experimental.
  • Permutation importance method can be used to compute feature importances for black box estimators.

Explanation and formatting are separated; you can get text-based explanation to display in console, HTML version embeddable in an IPython notebook or web dashboards, a pandas.DataFrame object if you want to process results further, or JSON version which allows to implement custom rendering and formatting on a client.

License is MIT.

Check docs for more.


define hyperiongray
A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures

A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures

Souvik Pratiher 16 Nov 17, 2021
👋🦊 Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.

👋🦊 Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.

DEEL 343 Jan 02, 2023
Visual Computing Group (Ulm University) 99 Nov 30, 2022
Neural network visualization toolkit for tf.keras

Neural network visualization toolkit for tf.keras

Yasuhiro Kubota 262 Dec 19, 2022
Logging MXNet data for visualization in TensorBoard.

Logging MXNet Data for Visualization in TensorBoard Overview MXBoard provides a set of APIs for logging MXNet data for visualization in TensorBoard. T

Amazon Web Services - Labs 327 Dec 05, 2022
Interactive convnet features visualization for Keras

Quiver Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras model = Model(...) Launch the vis

Keplr 1.7k Dec 21, 2022
python partial dependence plot toolbox

PDPbox python partial dependence plot toolbox Motivation This repository is inspired by ICEbox. The goal is to visualize the impact of certain feature

Li Jiangchun 722 Dec 30, 2022
Visual analysis and diagnostic tools to facilitate machine learning model selection.

Yellowbrick Visual analysis and diagnostic tools to facilitate machine learning model selection. What is Yellowbrick? Yellowbrick is a suite of visual

District Data Labs 3.9k Dec 30, 2022
Pytorch Feature Map Extractor

MapExtrackt Convolutional Neural Networks Are Beautiful We all take our eyes for granted, we glance at an object for an instant and our brains can ide

Lewis Morris 40 Dec 07, 2022
treeinterpreter - Interpreting scikit-learn's decision tree and random forest predictions.

TreeInterpreter Package for interpreting scikit-learn's decision tree and random forest predictions. Allows decomposing each prediction into bias and

Ando Saabas 720 Dec 22, 2022
ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments.

ModelChimp What is ModelChimp? ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments. ModelChimp provides the followi

ModelChimp 124 Dec 21, 2022
Model analysis tools for TensorFlow

TensorFlow Model Analysis TensorFlow Model Analysis (TFMA) is a library for evaluating TensorFlow models. It allows users to evaluate their models on

1.2k Dec 26, 2022
🎆 A visualization of the CapsNet layers to better understand how it works

CapsNet-Visualization For more information on capsule networks check out my Medium articles here and here. Setup Use pip to install the required pytho

Nick Bourdakos 387 Dec 06, 2022
Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Comprehensive collection of Pixel Attribution methods for Computer Vision.

Jacob Gildenblat 6.5k Jan 01, 2023
FairML - is a python toolbox auditing the machine learning models for bias.

======== FairML: Auditing Black-Box Predictive Models FairML is a python toolbox auditing the machine learning models for bias. Description Predictive

Julius Adebayo 338 Nov 09, 2022
Delve is a Python package for analyzing the inference dynamics of your PyTorch model.

Delve is a Python package for analyzing the inference dynamics of your PyTorch model.

Delve 73 Dec 12, 2022
Interpretability and explainability of data and machine learning models

AI Explainability 360 (v0.2.1) The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datase

1.2k Dec 29, 2022
Auralisation of learned features in CNN (for audio)

AuralisationCNN This repo is for an example of auralisastion of CNNs that is demonstrated on ISMIR 2015. Files auralise.py: includes all required func

Keunwoo Choi 39 Nov 19, 2022
L2X - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.

L2X Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation at ICML 2018,

Jianbo Chen 113 Sep 06, 2022
Algorithms for monitoring and explaining machine learning models

Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-qual

Seldon 1.9k Dec 30, 2022