Geochemistry Pi 开源软件 - 数据挖掘自动化 - 浙大远程实习

Posted 2023-03-02 Sany 何灿

Latest Update:

• Github: https://github.com/ZJUEarthData/geochemistrypi
• Gitee: https://gitee.com/zju-earth-data/geochemistrypi
  

Geochemistry π is a Python framework for data-driven geochemistry discovery. It provides an extendable tool and
one-stop shop for geochemical data analysis on tabular data. The goal of the Geochemistry π is to create
a series of user-friendly and extensible products of high automation for the full cycle of geochemistry research.

Key features are:

• Easy to use: The automation of data mining process provides the users with simple number options to choose.
• Extensible: It allows appending new algorithms through Scikit-learn with AutoML function by FLAML and Ray.

Quick Installation

One instruction to download on command line, such as Terminal on macOS, CMD on Windows.

pip install geochemistrypi

Note: The beta version runs on MacOS, Windows or Linux.

Example

How to run: After successfully downloading, run this instruction on command line whatever directory it is.

Case 1: Run with built-in data set for testing

geochemistrypi data-mining 

Note: There are four built-in data sets corresponding to four kinds of model pattern.

Case 2: Run with your own data set

geochemistrypi data-mining --data your_own_data_set.xlsx

Note: Currently, only .xlsx file is supported. Please specify the path your data file exists.

For more details: Please refer to

• Manual v1.1.0 for Geochemistry π - Beta (International - Google drive)
• Manual v1.1.0 for Geochemistry π - Beta (China - Tencent Docs)
• Geochemistry π - Download and Run the Beta Version (International - Youtube)
• Geochemistry π - Download and Run the Beta Version (China - Bilibili)

First Phase

It works as a software application with a command-line interface (CLI) to automate data mining process with
frequently-used machine learning algorithms and statistical analysis methods, which would further lower the
threshold for the geochemists.

The highlight is that through choosing simple number options, the users are able to implement a completed cycle of data
mining without knowledge of SciPy, NumPy, Pandas, Scikit-learn, FLAML, Ray packages.

Its data section, shown as below, provides feature engineering based on arithmatic operation. It allows the users
to have a statistic analysis on the data set as well as on the imputation result, which is supported by the combination
of Monte Carlo simulation and hypothesis testing.

Its models section provides both supervised learning and unsupervised learning methods from
Scikit-learn framework, including four types of algorithms, regression, classification,
clustering, and dimensional reduction. Integrated with FLAML and Ray framework, it allows the users to run
AutoML easily, fastly and cost-effectively on the built-in supervised learning algorithms in our framework.

The activity diagram of the Geochemistry π Version 1.0.0:

The whole package is under construction and the documentation is progressively evolving.

Team Info

Leader:

• Can He (Sany, National University of Singapore, Singapore)
  Email: [e-mail]

Core Developers:

• Jianhao Sun (Jin, China University of Geosciences, Wuhan, China)
• Jianming Zhao (Jamie, Jilin University, Changchun, China)
• Yang Lyu (Daisy, Zhejiang University, China)
• Shengxin Wang (Samson, Lanzhou University, China)

Members:

• Wenyu Zhao (Molly, Zhejiang University, China)
• Fang Li (liv, Shenzhen University, China)
• Ting Liu (Kira, Sun Yat-sen University, China)
• Kaixin Zheng (Hayne, Sun Yat-sen University, China)
• Aixiwake·Janganuer (Ayshuak, Sun Yat-sen University, China)
• Parnanjan Dutta (Presidency University, Kolkata, India)
• Bailun Jiang (EPSI / Lille University, France)
• Yongkang Chang (Kill-virus, Langzhou University, China)
• Xirui Zhu (Rae, University of York, United Kingdom)

Join Us 😃

The recruitment of research interns is ongoing !!!

Key Point: All things are done online, remote work (*^▽*)

What can you learn?

• Learning the full cycle of data mining on tabular data, including the algorithms in regression,
  classification, clustering, and decomposition.
• Learning to be a qualified Python developer, including any Python programing contents towards data mining, basic software engineering techniques like OOP developing, and cooperation tools like Git.

What can you get?

• Research internship proof and reference letter after working for > 200 hours.
• Chance to pay a visit to Hangzhou, China, sponsored by ZJU Earth Data.
• Chance to be guided by the experts from IT companies in Silicon Valley and Hangzhou.
• Bonus depending on your performance.

Current Working Pattern:

• Online working and cooperation
• Three weeks per working cycle -> One online meeting per working cycle
• One cycle report (see below) per cycle - 5 mins to finish

Even if you are not familiar with topics above, but if you are interested in and have plenty of time to do it.
That’s enough. We have a full-developed training system to help you, as a newbie of data mining or Python developer,
learn steps by steps with seniors until you can make a significant contribution to our project.

More details about the project?
Please refer to:
English Page: https://person.zju.edu.cn/en/zhangzhou
Chinese Page: https://person.zju.edu.cn/zhangzhou#0

Do you want to contribute to this open-source program?
Contact with your CV: [e-mail]

In-house Materials

Materials are in both Chinese and English. Others unshown below are internal materials.

1. Guideline Manual – Geochemistry π (International - Google drive)
2. Guideline Manual – Geochemistry π (China - Tencent Docs)
3. Learning Steps for Newbies – Geochemistry π (International - Google drive)
4. Learning Steps for Newbies - Geochemistry π (China - Tencent Docs)
5. Code Specification v2.1.2 - Geochemistry π (International - Google drive)
6. Code Specification v2.1.2 - Geochemistry π (China - Tencent Docs)
7. Cycle Report - Geochemistry π (International - Google drive)
8. Cycle Report - Geochemistry π (China - Tencent Docs)

In-house Videos

Technical record videos are on Bilibili and Youtube synchronously while other meeting videos are internal materials.
More Videos will be recorded soon.

1. ZJU_Earth_Data Introduction (Geochemical Data, Python, Geochemistry π) - Prof. Zhang
2. How to Collaborate and Provide Bug Report on Geochemistry π Through GitHub - Can He (Sany)
3. Geochemistry π - Download and Run the Beta Version
4. How to Create and Use Virtual Environment on Geochemistry π - Can He (Sany)
5. How to use Github-Desktop in conflict resolution - Qiuhao Zhao (Brad)
6. Virtual Environment & Packages On Windows - Jianming Zhao (Jamie)
7. Git Workflow & Coordinating Synchronization - Jianming Zhao (Jamie)

Contributors

• Qiuhao Zhao (Brad, Zhejiang University, China)
• Anzhou Li (Andrian, Zhejiang University, China)
• Xunxin Liu (Tante, China University of Geosciences, Wuhan, China)
• Xin Li (The University of Manchester, United Kingdom)