Skip to main content

Some nice open-source python repositories to work

I have been looking to contribute in some nice data science repositories in python. I have searched some nice repos which are in their begining phases or rather they are in a position that they can be touched to improve and amateurs like me in machine learning with almost no commit in open sources, can do a pretty good job committing to those. Keeping that in mind, I am starting to list down some of the repos like that:

(1) dython:This is a python data tools repo. Although this is published now and has somewhat no issues; it looks like being somewhat open-ended creative people can add a lots of other tools and functionality to this tool, make it more main streamed and therefore increase their individual and the package's overall progress.

(2)simpletransformers : This is simple transformer written by Thilina Rajapakse. This is an amazing package which uses the huggingface's transformer library and then combines its high level knowledge requirements into its intrinsic program and then tones it down as much as to a small level as sklearn packages. This, being a great package, is ridden with numbers of issues and therefore an interested contributor should be able to solve some of them and make some code correction commit as well as small functionality development to this repository.
If you are already working with nlp and transformers; know the transformers library really well, then you are already in advantage to work on this repo. Head to the link and start solving some of the issues today!

(3)pyGAM : This is pygam written by daniel serven martin (spelling maybe wrong). This is a package for implementing GAM model in python; being a alternative for the mgcv package in R written by simon wood. This package is really good, elaborate and contains complex coding exercises. But pygam, being the interesting package it can be, has yet a lot of road to progress to become at par with mgcv. So people who are interested in statistical programming, can definitely try and contribute to such a complex repository.

(4)scikit-tda: This is scikit_tda, built for topological data analysis. This is intended for non-topologist people. Being a bit math heavy and topology "infested", topological data analysis is a bit hard to start with. This library sort of tries to solve that issue. With the 4 membered team, this is a growing and interesting repository. But I still don't know what are the requirements for contributing to core code, although some examples and documentation as well as question issue resolving can be done by interested beginners. Go to the link and try to explore the code more.

This is a small list yet I know, and will develop as I find growing and amazing repos like the ones I noted here.
Labels:

Comments

Post a Comment

Popular posts from this blog

20 Must-Know Math Puzzles for Data Science Interviews: Test Your Problem-Solving Skills

Introduction:   When preparing for a data science interview, brushing up on your coding and statistical knowledge is crucial—but math puzzles also play a significant role. Many interviewers use puzzles to assess how candidates approach complex problems, test their logical reasoning, and gauge their problem-solving efficiency. These puzzles are often designed to test not only your knowledge of math but also your ability to think critically and creatively. Here, we've compiled 20 challenging yet exciting math puzzles to help you prepare for data science interviews. We’ll walk you through each puzzle, followed by an explanation of the solution. 1. The Missing Dollar Puzzle Puzzle: Three friends check into a hotel room that costs $30. They each contribute $10. Later, the hotel realizes there was an error and the room actually costs $25. The hotel gives $5 back to the bellboy to return to the friends, but the bellboy, being dishonest, pockets $2 and gives $1 back to each friend. No...
Post a Comment
Read more

Spacy errors and their solutions

 Introduction: There are a bunch of errors in spacy, which never makes sense until you get to the depth of it. In this post, we will analyze the attribute error E046 and why it occurs. (1) AttributeError: [E046] Can't retrieve unregistered extension attribute 'tag_name'. Did you forget to call the set_extension method? Let's first understand what the error means on superficial level. There is a tag_name extension in your code. i.e. from a doc object, probably you are calling doc._.tag_name. But spacy suggests to you that probably you forgot to call the set_extension method. So what to do from here? The problem in hand is that your extension is not created where it should have been created. Now in general this means that your pipeline is incorrect at some level.  So how should you solve it? Look into the pipeline of your spacy language object. Chances are that the pipeline component which creates the extension is not included in the pipeline. To check the pipe eleme...
Post a Comment
Read more

GAM model : PyGAM package details Analysis and possible issue resolving

Introduction:                  picture credit to peter laurinec. I have been studying about PyGAM package for last couple of days. Now, I am planning to thoroughly analyze the code of PyGAM package with necessary description of GAM model and sources whenever necessary. This is going to be a long post and very much technical in nature. Pre-requisites: For understanding the coding part of PyGAM package, first you have to learn what is a GAM model. GAM stands for generalized additive model, i.e. it is a type of statistical modeling where a target variable Y is roughly represented by additive combination of set of different functions. In formula it can be written as: g(E[Y]) = f 1 (x 1 ) + f 2 (x 2 ) + f 3 (x 3 ,x 4 )+...etc where g is called a link function and f are different types of functions. In technical terms, in GAM model, theoretically expectation of the link transformed target variable is assume...
Post a Comment
Read more