Welcome to Geochemistry π

Home

• Introduction
  • What it is
  • First Phase
  • Second Phase
  • In-house Materials
  • In-house Videos
• Change Log
  • [Unreleased]
  • [0.4.0] - 2023-12-15
    • Added
    • Changed
  • [0.3.0] - 2023-08-11
    • Added
  • [0.2.1] - 2023-05-01
    • Fixed
  • [0.2.0] - 2023-04-19
    • Added
  • [0.1.0] - 2023-02-01
    • Added

For User

• User Guide
  • 1. Installation
    • 1.1 Check Environment
    • 1.2 Quick Install
    • 1.3 Advanced Install
  • 2. Example
  • 3. Data Format
  • 4. Use
    • 4.1 Selection algorithm
    • 4.2 World Map Projection for A Specific Element Option
    • 4.3 Do you want to continue to project a new element in the World Map
    • 4.4 Select the data range you want to process
    • 4.5 Strategy for Missing Values
    • 4.6 Hypothesis Testing on Imputation Method
    • 4.7 Feature Engineering
    • 4.8 Do you want to continue to construct a new feature
    • 4.9 Mode Options
    • 4.10 Data Split - X Set and Y Set
    • 4.11 Feature Scaling on X Set
    • 4.12 Data Split - Train Set and Test Set
    • 4.13 Model Selection
  • 5. Bug Report
  • 6. How to use pickle and joblib
  • 7. Advice
• Installation Manual
  • 1. Preparation
    • 1.1 Install Python Interpreter
    • 1.2 Install Conda
  • 2. Download Geochemistry π in Virtual Environment
    • 2.1 Create A Virtual Environment
    • 2.2 Use pip to Download
  • 3. Solutions and Suggestions for Installation Failure
    • 3.1 Use Tsinghua Mirror Source
    • 3.2 Use ‘pip install -r requirements/production.txt’
    • 3.3 Report An Error to Our Team
• Model Example
  • Data Preprocessing
    • Loading Data
    • World Map Projection
    • Statistical Summary
    • Missing Value
    • Feature Engineering
  • Classification
    • 1. Train-Test Data Preparation
    • 2. Model Selection
    • 3. Results
  • Regression
    • 1. Introduction to Regression
    • 2. Introduction to Regression function of Geochemistry π
    • 3. Regression Model-Running
  • Clustering
    • Modle Selection
    • Hyper-Parameters Specification
    • Results
    • 2 dimensions graphs of data
    • 3 dimensions graphs of data
  • Decomposition
    • T-distributed Stochastic Neighbor Embedding （T-SNE）
• Contact Us
  • Bug Report
  • Bug Template
  • Join Us
  • Q&A
• Docs Link
  • 1. Geochemistry π Project Introduction
  • 2. Code Download Link
  • 3. Installing and Model Running Video
  • 4. Installing and Model Running Docs
  • 5. Users Feedback Doc

For Developer

• Developer Guide
  • 1. Installation
    • 1.1 Check Environment
    • 1.2 Quick Install
    • 1.3 Advanced Install
  • 2. Example
  • 3. Data Format
  • 4. Use
    • 4.1 Selection algorithm
    • 4.2 World Map Projection for A Specific Element Option
    • 4.3 Do you want to continue to project a new element in the World Map
    • 4.4 Select the data range you want to process
    • 4.5 Strategy for Missing Values
    • 4.6 Hypothesis Testing on Imputation Method
    • 4.7 Feature Engineering
    • 4.8 Do you want to continue to construct a new feature
    • 4.9 Mode Options
    • 4.10 Data Split - X Set and Y Set
    • 4.11 Feature Scaling on X Set
    • 4.12 Data Split - Train Set and Test Set
    • 4.13 Model Selection
  • 5. Bug Report
  • 6. Advice
• Contributing
  • Contributors
    • Leader:
    • Technical Group:
    • Product Group:
    • Former Contributors:
• Local Deployment
• Docker Deployment
• Complete Pull Request
• Add New Model To Framework
  • Table of Contents
  • 1. Understand the model
  • 2. Add Model
    • 2.1 Add The Model Class
    • 2.2 Add AutoML
    • 2.3 Get the hyperparameter value through interactive methods
    • 2.4 Call Model
    • 2.5 Add the algorithm list and set NON_AUTOML_MODELS
  • 3. Test model
  • 4. Completed Pull Request
  • 5. Precautions
• Docs Link
  • 1. Project’s Share Docs
  • 2. Technical Video (in Bilibili app)
  • 3. Leanring Materials in Nutstore
    • Folders Introduction
    • Improtant Files in Nutstore
  • 4. Member Notice

Indices and tables

• Index

• Module Index

• Search Page