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 Installing the requirements.txt may generate some error due to outdated MS Visual C++ Build.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
PyTorch implementation of the ideas presented in the paper Interaction Grounded Learning (IGL)

Interaction Grounded Learning This repository contains a simple PyTorch implementation of the ideas presented in the paper Interaction Grounded Learni

Arthur Juliani 4 Aug 31, 2022
A simple Python library for stochastic graphical ecological models

What is Viridicle? Viridicle is a library for simulating stochastic graphical ecological models. It implements the continuous time models described in

Theorem Engine 0 Dec 04, 2021
The codebase for our paper "Generative Occupancy Fields for 3D Surface-Aware Image Synthesis" (NeurIPS 2021)

Generative Occupancy Fields for 3D Surface-Aware Image Synthesis (NeurIPS 2021) Project Page | Paper Xudong Xu, Xingang Pan, Dahua Lin and Bo Dai GOF

xuxudong 97 Nov 10, 2022
This is the official implementation of our proposed SwinMR

SwinMR This is the official implementation of our proposed SwinMR: Swin Transformer for Fast MRI Please cite: @article{huang2022swin, title={Swi

A Yang Lab (led by Dr Guang Yang) 27 Nov 17, 2022
[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
QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing Environment Tested on Ubuntu 14.04 64bit and 16.04 64bit Installation # disabl

gts3.org (<a href=[email protected])"> 581 Dec 30, 2022
Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021

LoFTR: Detector-Free Local Feature Matching with Transformers Project Page | Paper LoFTR: Detector-Free Local Feature Matching with Transformers Jiami

ZJU3DV 1.4k Jan 04, 2023
Deep Learning Specialization by Andrew Ng, deeplearning.ai.

Deep Learning Specialization on Coursera Master Deep Learning, and Break into AI This is my personal projects for the course. The course covers deep l

Engen 1.5k Jan 07, 2023
PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

Daft-Exprt - PyTorch Implementation PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis The

Keon Lee 47 Dec 18, 2022
Repository for benchmarking graph neural networks

Benchmarking Graph Neural Networks Updates Nov 2, 2020 Project based on DGL 0.4.2. See the relevant dependencies defined in the environment yml files

NTU Graph Deep Learning Lab 2k Jan 03, 2023
Joint Learning of 3D Shape Retrieval and Deformation, CVPR 2021

Joint Learning of 3D Shape Retrieval and Deformation Joint Learning of 3D Shape Retrieval and Deformation Mikaela Angelina Uy, Vladimir G. Kim, Minhyu

Mikaela Uy 38 Oct 18, 2022
Release of the ConditionalQA dataset

ConditionalQA Datasets accompanying the paper ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers. Disclaimer This dataset

14 Oct 17, 2022
Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Deep Learning to Improve Breast

Li Shen 305 Jan 03, 2023
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations Project | Paper | Colab PyTorch implementation of SDEdit: Image Synthesis a

536 Jan 05, 2023
Various operations like path tracking, counting, etc by using yolov5

Object-tracing-with-YOLOv5 Various operations like path tracking, counting, etc by using yolov5

Pawan Valluri 5 Nov 28, 2022
Distributional Sliced-Wasserstein distance code

Distributional Sliced Wasserstein distance This is a pytorch implementation of the paper "Distributional Sliced-Wasserstein and Applications to Genera

VinAI Research 39 Jan 01, 2023
Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs

Project Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs, https://arxiv.org/pdf/2111.01940.pdf. Authors Truong Son Hy

5 Jun 28, 2022
A really easy-to-use and powerful sudoku solver.

SodukuSolver This is a really useful sudoku solver with a Qt gui. USAGE Enter the numbers in and click "RUN"! If you don't want to wait, simply press

Ujhhgtg Teams 11 Jun 02, 2022
Off-policy continuous control in PyTorch, with RDPG, RTD3 & RSAC

arXiv technical report soon available. we are updating the readme to be as comprehensive as possible Please ask any questions in Issues, thanks. Intro

Zhihan 31 Dec 30, 2022
Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling

⚠️ ‎‎‎ A more recent and actively-maintained version of this code is available in ivadomed Stacked Hourglass Network with a Multi-level Attention Mech

Reza Azad 14 Oct 24, 2022