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.
Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)
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Automatic neural network visualizations generated in your browser!
The Bias and Fairness Audit Toolkit Aequitas is an open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers
tensorboardX Write TensorBoard events with simple function call. The current release (v2.1) is tested on anaconda3, with PyTorch 1.5.1 / torchvision 0
Yellowbrick Visual analysis and diagnostic tools to facilitate machine learning model selection. What is Yellowbrick? Yellowbrick is a suite of visual
Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Comprehensive collection of Pixel Attribution methods for Computer Vision.
⬛ PyCEbox Python Individual Conditional Expectation Plot Toolbox A Python implementation of individual conditional expecation plots inspired by R's IC
Anchor This repository has code for the paper High-Precision Model-Agnostic Explanations. An anchor explanation is a rule that sufficiently “anchors”
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