Python Auto-ML Package for Tabular Datasets

Overview
Tabular-AutoML

Tabular-AutoML

AutoML Package for tabular datasets

Tabular dataset tuning is now hassle free!

Run one liner command and get best tuning and processed dataset in a go.

Python Git

Used Python Libraries :
lightgbm numpy numpy numpy

Installation & Usage


  1. Create a Virtual Environment : Tutorial
  2. Clone the repository.
  3. Open the directory with cmd.
  4. Copy this command in terminal to install dependencies.
pip install -r requirements.txt
  1. Installing the requirements.txt may generate some error due to outdated MS Visual C++ Build. You can fix this problem using this.
  2. First check the parser variable that has to be passed with all customizations.
>>> python -m tab_automl.main --help
usage: main.py [-h] -d  -t  -tf  [-p] [-f] [-spd] [-sfd] [-sm]

automl hyper parameters

optional arguments:
  -h, --help            show this help message and exit
  -d , --data-source    File path
  -t , --problem-type   Problem Type , currently supporting *regression* or *classification*
  -tf , --target-feature
                        Target feature inside the data
  -p , --pre-proc       If data processing is required
  -f , --fet-eng        If feature engineering is required
  -spd , --save-proc-data
                        Save the processed data
  -sfd , --save-fet-data
                        Save the feature engineered data
  -sm , --save-model    Save the best trained model
  1. Now run the command with your custom data, problem type and target feature
>> # For Classification Problem >>> python -m tab_automl.main -d "your custom data scource\custom_data.csv" -t "classification" -tf "your_custom_target_feature" -spd "true" -sfd "true" -sm "true"">
>>> # For Regression Problem
>>> python -m tab_automl.main -d "your custom data scource\custom_data.csv" -t "regression" -tf "your_custom_target_feature" -spd "true" -sfd "true" -sm "true"

>>> # For Classification Problem
>>> python -m tab_automl.main -d "your custom data scource\custom_data.csv" -t "classification" -tf "your_custom_target_feature" -spd "true" -sfd "true" -sm "true"

Contributing Guidelines


  1. Coment on the issue on which you want to work.
  2. If you get assigned, fork the repository.
  3. Create a new branch which should be named on your github user_id , e.g. sagnik1511.
  4. Update the changes on that branch.
  5. Create a PR (Pull request) to the main branch of the parent repository.
  6. The PR title should named like this [Issue Number] Heading of the issue.
  7. Describe the changes you have done with proper reasons.

Contributors


  1. Sagnik Roy : sagnik1511

If you like the project, do

Also follow me on GitHub , Kaggle , LinkedIn

Thank You for Visiting :)

Owner
Sagnik Roy
Data Science Intern @ Argoid • Video Games & Machine Vision attracts me!
Sagnik Roy
RTSeg: Real-time Semantic Segmentation Comparative Study

Real-time Semantic Segmentation Comparative Study The repository contains the official TensorFlow code used in our papers: RTSEG: REAL-TIME SEMANTIC S

Mennatullah Siam 592 Nov 18, 2022
A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).

APPNP ⠀ A PyTorch implementation of Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019). Abstract Neural message pass

Benedek Rozemberczki 329 Dec 30, 2022
A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science

PyGrid is a peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft. PyGrid is also the central serv

OpenMined 615 Jan 03, 2023
Steerable discovery of neural audio effects

Steerable discovery of neural audio effects Christian J. Steinmetz and Joshua D. Reiss Abstract Applications of deep learning for audio effects often

Christian J. Steinmetz 182 Dec 29, 2022
RL-GAN: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

RL-GAN: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation RL-GAN is an official implementation of the paper: T

42 Nov 10, 2022
Official Implementation of Swapping Autoencoder for Deep Image Manipulation (NeurIPS 2020)

Swapping Autoencoder for Deep Image Manipulation Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang UC

449 Dec 27, 2022
GEP (GDB Enhanced Prompt) - a GDB plug-in for GDB command prompt with fzf history search, fish-like autosuggestions, auto-completion with floating window, partial string matching in history, and more!

GEP (GDB Enhanced Prompt) GEP (GDB Enhanced Prompt) is a GDB plug-in which make your GDB command prompt more convenient and flexibility. Why I need th

Alan Li 23 Dec 21, 2022
This is the code for the paper "Contrastive Clustering" (AAAI 2021)

Contrastive Clustering (CC) This is the code for the paper "Contrastive Clustering" (AAAI 2021) Dependency python=3.7 pytorch=1.6.0 torchvision=0.8

Yunfan Li 210 Dec 30, 2022
SCU OlympicsRunning Baseline

Competition 1v1 running Environment check details in Jidi Competition RLChina2021智能体竞赛 做出的修改: 奖励重塑:修改了环境,重新设置了奖励的分配,使得奖励组成不只有零和博弈,还有探索环境的奖励。 算法微调:修改了官

ZiSeoi Wong 2 Nov 23, 2021
Pytorch implementation for "Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion" (NeurIPS 2021)

Density-aware Chamfer Distance This repository contains the official PyTorch implementation of our paper: Density-aware Chamfer Distance as a Comprehe

Tong WU 93 Dec 15, 2022
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning

TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning Authors: Yixuan Su, Fangyu Liu, Zaiqiao Meng, Lei Shu, Ehsan Shareghi, and Nig

Yixuan Su 79 Nov 04, 2022
A PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-Supervised Learning Framework".

Mugs: A Multi-Granular Self-Supervised Learning Framework This is a PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-

Sea AI Lab 62 Nov 08, 2022
Weight initialization schemes for PyTorch nn.Modules

nninit Weight initialization schemes for PyTorch nn.Modules. This is a port of the popular nninit for Torch7 by @kaixhin. ##Update This repo has been

Alykhan Tejani 69 Jan 26, 2021
Code for Paper: Self-supervised Learning of Motion Capture

Self-supervised Learning of Motion Capture This is code for the paper: Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki, Self-sup

Hsiao-Yu Fish Tung 87 Jul 25, 2022
Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification.

Easy Few-Shot Learning Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification. This repository is made for you

Sicara 399 Jan 08, 2023
Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.

mtomo Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation.

Katsuya Hyodo 24 Mar 02, 2022
PyTorch reimplementation of REALM and ORQA

PyTorch reimplementation of REALM and ORQA

Li-Huai (Allan) Lin 17 Aug 20, 2022
This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"

Differentiable Volumetric Rendering Paper | Supplementary | Spotlight Video | Blog Entry | Presentation | Interactive Slides | Project Page This repos

697 Jan 06, 2023
Parametric Contrastive Learning (ICCV2021)

Parametric-Contrastive-Learning This repository contains the implementation code for ICCV2021 paper: Parametric Contrastive Learning (https://arxiv.or

DV Lab 156 Dec 21, 2022
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

SASSnet Code for paper: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images(MICCAI 2020) Our code is origin from UA-MT You can fin

klein 125 Jan 03, 2023