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
Open Source Differentiable Computer Vision Library for PyTorch

Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer

kornia 7.6k Jan 04, 2023
Run object detection model on the Raspberry Pi

Using TensorFlow Lite with Python is great for embedded devices based on Linux, such as Raspberry Pi.

Dimitri Yanovsky 6 Oct 08, 2022
Signals-backend - A suite of card games written in Python

Card game A suite of card games written in the Python language. Features coming

1 Feb 15, 2022
Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

Yan Yuanmeng 478 Dec 25, 2022
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali

Stanford Intelligent Systems Laboratory 9 Jun 06, 2022
JudeasRx - graphical app for doing personalized causal medicine using the methods invented by Judea Pearl et al.

JudeasRX Instructions Read the references given in the Theory and Notation section below Fire up the Jupyter Notebook judeas-rx.ipynb The notebook dra

Robert R. Tucci 19 Nov 07, 2022
Convert game ISO and archives to CD CHD for emulation on Linux.

tochd Convert game ISO and archives to CD CHD for emulation. Author: Tuncay D. Source: https://github.com/thingsiplay/tochd Releases: https://github.c

Tuncay 20 Jan 02, 2023
[CIKM 2021] Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. This repo contains the PyTorch code and implementation for the paper E

Akuchi 18 Dec 22, 2022
Fuzzy Overclustering (FOC)

Fuzzy Overclustering (FOC) In real-world datasets, we need consistent annotations between annotators to give a certain ground-truth label. However, in

2 Nov 08, 2022
Starter kit for getting started in the Music Demixing Challenge.

Music Demixing Challenge - Starter Kit ๐Ÿ‘‰ Challenge page This repository is the Music Demixing Challenge Submission template and Starter kit! Clone th

AIcrowd 106 Dec 20, 2022
Pytorch implementation of SimSiam Architecture

SimSiam-pytorch A simple pytorch implementation of Exploring Simple Siamese Representation Learning which is developed by Facebook AI Research (FAIR)

Saeed Shurrab 1 Oct 20, 2021
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at .

PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f

Aayush Bansal 196 Aug 10, 2022
TextureGAN in Pytorch

TextureGAN This code is our PyTorch implementation of TextureGAN [Project] [Arxiv] TextureGAN is a generative adversarial network conditioned on sketc

Patsorn 147 Dec 14, 2022
FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well i

0 Sep 06, 2022
GAN Image Generator and Characterwise Image Recognizer with python

MODEL SUMMARY ๋ชจ๋ธ์˜ ๊ตฌ์กฐ๋Š” ํฌ๊ฒŒ 6๋‹จ๊ณ„๋กœ ๋‚˜๋‰ฉ๋‹ˆ๋‹ค. STEP 0: Input Image Predict ํ•  ์ด๋ฏธ์ง€๋ฅผ ๋ชจ๋ธ์— ์ž…๋ ฅํ•ฉ๋‹ˆ๋‹ค. STEP 1: Make Black and White Image STEP 1 ์€ ์ž…๋ ฅ๋ฐ›์€ ์ด๋ฏธ์ง€์˜ ๊ธ€์ž๋ฅผ ํ‘์ƒ‰์œผ๋กœ, ๋ฐฐ๊ฒฝ์„

Juwan HAN 1 Feb 09, 2022
Wind Speed Prediction using LSTMs in PyTorch

Implementation of Deep-Forecast using PyTorch Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting Adapted from original implementation Setu

Onur Kaplan 151 Dec 14, 2022
Synthetic LiDAR sequential point cloud dataset with point-wise annotations

SynLiDAR dataset: Learning From Synthetic LiDAR Sequential Point Cloud This is official repository of the SynLiDAR dataset. For technical details, ple

78 Dec 27, 2022
Neural Dynamic Policies for End-to-End Sensorimotor Learning

This is a PyTorch based implementation for our NeurIPS 2020 paper on Neural Dynamic Policies for end-to-end sensorimotor learning.

Shikhar Bahl 47 Dec 11, 2022
Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

Semantic-NeRF: Semantic Neural Radiance Fields Project Page | Video | Paper | Data In-Place Scene Labelling and Understanding with Implicit Scene Repr

Shuaifeng Zhi 243 Jan 07, 2023
A Python package to create, run, and post-process MODFLOW-based models.

Version 3.3.5 โ€” release candidate Introduction FloPy includes support for MODFLOW 6, MODFLOW-2005, MODFLOW-NWT, MODFLOW-USG, and MODFLOW-2000. Other s

388 Nov 29, 2022