Fit Fast, Explain Fast

Overview

FastExplain

Fit Fast, Explain Fast

Installing

pip install fast-explain

About FastExplain

FastExplain provides an out-of-the-box tool for analysts to quickly explore data, with flexibility to fine-tune if needed.

  • Automated cleaning and fitting of machine learning models with hyperparameter search
  • Aesthetic display of explanatory methods ready for reporting
  • Connected interface for all data, models and related explanatory methods

Quickstart

Automated Cleaning and Fitting

from FastExplain import model_data
from FastExplain.datasets import load_titanic_data
df = load_titanic_data()
classification = model_data(df, 'Survived', hypertune=True)

Aesthetic Display

from FastExplain.explain import plot_one_way_analysis, plot_ale
plot_one_way_analysis(classification.data.df, "Age", "Survived", filter = "Sex == 1")

One Way

plot_ale(classification.m, classification.data.xs, "Age", filter = "Sex == 1", dep_name = "Survived")

ALE

classification_1 = model_data(df, 'Survived', cont_names=['Age'], cat_names = [])
models = [classification.m, classification_1.m]
data = [classification.data.xs, classification_1.data.xs]
plot_ale(models, data, 'Age', dep_name = "Survived")

multi_ALE

Connected Interface

classification.plot_one_way_analysis("Age", filter = "Sex == 1")
classification.plot_ale("Age", filter = "Sex == 1")
classification.shap_dependence_plot("Age", filter = "Sex == 1")

SHAP

classification.error
# {'auc': {'model': {'train': 0.9934332941166654,
# 'val': 0.8421607378129118,
# 'overall': 0.9665739941840028}},
# 'cross_entropy': {'model': {'train': 0.19279692001978943,
# 'val': 0.4600233891109683,
# 'overall': 0.24648214781700722}}}

Models Supported

  • Random Forest
  • XGBoost
  • Explainable Boosting Machine
  • ANY Model Class with fit and predict attributes

Exploratory Methods Supported:

  • One-way Analysis
  • Two-way Analysis
  • Feature Importance Plots
  • ALE Plots
  • Explainable Boosting Methods
  • SHAP Values
  • Partial Dependence Plots
  • Sensitivity Analysis
Implementation of paper "DCS-Net: Deep Complex Subtractive Neural Network for Monaural Speech Enhancement"

DCS-Net This is the implementation of "DCS-Net: Deep Complex Subtractive Neural Network for Monaural Speech Enhancement" Steps to run the model Edit V

Jack Walters 10 Apr 04, 2022
3rd Place Solution of the Traffic4Cast Core Challenge @ NeurIPS 2021

3rd Place Solution of Traffic4Cast 2021 Core Challenge This is the code for our solution to the NeurIPS 2021 Traffic4Cast Core Challenge. Paper Our so

7 Jul 25, 2022
Related resources for our EMNLP 2021 paper

Plan-then-Generate: Controlled Data-to-Text Generation via Planning Authors: Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, and Nigel Collier Code

Yixuan Su 61 Jan 03, 2023
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
[NeurIPS'21] Shape As Points: A Differentiable Poisson Solver

Shape As Points (SAP) Paper | Project Page | Short Video (6 min) | Long Video (12 min) This repository contains the implementation of the paper: Shape

394 Dec 30, 2022
Python implementation of the multistate Bennett acceptance ratio (MBAR)

pymbar Python implementation of the multistate Bennett acceptance ratio (MBAR) method for estimating expectations and free energy differences from equ

Chodera lab // Memorial Sloan Kettering Cancer Center 169 Dec 02, 2022
Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

Peidong Liu(刘沛东) 54 Dec 17, 2022
A PyTorch-based library for fast prototyping and sharing of deep neural network models.

A PyTorch-based library for fast prototyping and sharing of deep neural network models.

78 Jan 03, 2023
Robotic Process Automation in Windows and Linux by using Driagrams.net BPMN diagrams.

BPMN_RPA Robotic Process Automation in Windows and Linux by using BPMN diagrams. With this Framework you can draw Business Process Model Notation base

23 Dec 14, 2022
A PyTorch Implementation of "Neural Arithmetic Logic Units"

Neural Arithmetic Logic Units [WIP] This is a PyTorch implementation of Neural Arithmetic Logic Units by Andrew Trask, Felix Hill, Scott Reed, Jack Ra

Kevin Zakka 181 Nov 18, 2022
clustimage is a python package for unsupervised clustering of images.

clustimage The aim of clustimage is to detect natural groups or clusters of images. Image recognition is a computer vision task for identifying and ve

Erdogan Taskesen 52 Jan 02, 2023
Mixed Transformer UNet for Medical Image Segmentation

MT-UNet Update 2022/01/05 By another round of training based on previous weights, our model also achieved a better performance on ACDC (91.61% DSC). W

dotman 92 Dec 25, 2022
Contrastive Learning for Compact Single Image Dehazing, CVPR2021

AECR-Net Contrastive Learning for Compact Single Image Dehazing, CVPR2021. Official Pytorch based implementation. Paper arxiv Pytorch Version TODO: mo

glassy 253 Jan 01, 2023
CVPR2021: Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Temporal Context Aggregation Network - Pytorch This repo holds the pytorch-version codes of paper: "Temporal Context Aggregation Network for Temporal

Zhiwu Qing 63 Sep 27, 2022
This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

2.4k Jan 08, 2023
Doosan robotic arm, simulation, control, visualization in Gazebo and ROS2 for Reinforcement Learning.

Robotic Arm Simulation in ROS2 and Gazebo General Overview This repository includes: First, how to simulate a 6DoF Robotic Arm from scratch using GAZE

David Valencia 12 Jan 02, 2023
Face and other object detection using OpenCV and ML Yolo

Object-and-Face-Detection-Using-Yolo- Opencv and YOLO object and face detection is implemented. You only look once (YOLO) is a state-of-the-art, real-

Happy N. Monday 3 Feb 15, 2022
StyleGAN2-ADA - Official PyTorch implementation

Need Help? If you’re new to StyleGAN2-ADA and looking to get started, please check out this video series from a course Lia Coleman and I taught in Oct

Derrick Schultz 217 Jan 04, 2023
A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

img_sussifier A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196 Examples How to use install python pip i

41 Sep 30, 2022
MIMO-UNet - Official Pytorch Implementation

MIMO-UNet - Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Rethinking Coarse-to-

Sungjin Cho 248 Jan 02, 2023