This is a simple package wrapping py3dmol for a single command visualization of a RDKit molecule and its conformations (embed as Conformer objects in the Molecule)
pip install molconfviewer
from molconfviewer import MolConfViewer
mol_conf_viewer = MolConfViewer()
mol_conf_viewer.view(mol=mol) # where mol is a rdkit mol
See the MolConfViewer object code to customize the visualization. For more possibilities, please check py3dmol and 3dmol.js.
GNNLens2 is an interactive visualization tool for graph neural networks (GNN).
======== FairML: Auditing Black-Box Predictive Models FairML is a python toolbox auditing the machine learning models for bias. Description Predictive
Themis ML themis-ml is a Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms. Fairness-aware M
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Neural-Backed Decision Trees · Site · Paper · Blog · Video Alvin Wan, *Lisa Dunlap, *Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah
tensorboardX Write TensorBoard events with simple function call. The current release (v2.1) is tested on anaconda3, with PyTorch 1.5.1 / torchvision 0
Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, TensorFlow Lite, Keras, Caffe, Darknet, ncnn,
Convolutional Neural Network Visualizations This repository contains a number of convolutional neural network visualization techniques implemented in
L2X Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation at ICML 2018,
Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The
Hierarchical neural-net interpretations (ACD) 🧠 Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Offic
JittorVis - Visual understanding of deep learning model.
A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures
lime This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predict
Lucid Lucid is a collection of infrastructure and tools for research in neural network interpretability. We're not currently supporting tensorflow 2!
neural-dream This is a PyTorch implementation of DeepDream. The code is based on neural-style-pt. Here we DeepDream a photograph of the Golden Gate Br
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo
A collection of research papers and software related to explainability in graph machine learning.
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