Changelog¶
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
[Unreleased]¶
MLOps core of continuous training in web interface
[0.5.0] - 2023-01-14¶
Added¶
Missing value process with three options
Fixed random state for all models
New Models:
Regression Models
Bayesian Ridge Regression
Clustering Models
Agglomerative Clustering
Changed¶
Renamed command to implement model inference
[0.4.0] - 2023-12-15¶
Added¶
MLOps core of model inference in command line interface using transformer pipeline
Multi-class label and binary label training for all classification models
CSV data file import
Reduced data storage in decomposition
Data selection function with null, space and Chinese parentheses dection functionality
label customization in classification
Feature selection function
Design diagrams of the whole project
Feature scaling for unsupervised learning
Built-in inference dataset loading
Silhouette score frequency diagram for all clustering model
Two clustering model score for all clustering model
New Models:
Regression Models
Elastic Net
Stochastic Gradient Regression
Classification Models
Gradient Boosting
K-Nearest Neighbors
Stochastic Gradient Descent
Changed¶
Lasso regression model with automatic parameter tuning functionality
[0.3.0] - 2023-08-11¶
Added¶
Colourful command line interface to highligh importance stuffs.
Standardization of run-driven operation for an experiment.
Specialized storage mechanism to achieve the MLOps core of machine learning lifecycle management using MLflow
Online documentation, including project section, user section, developer section.
New Models:
Regression Models
Lasso Regression
Gradient Boosting
K-Nearest Neighbors
Decomposition Models
T-SNE
MDS
Docker deployment configuration.
Continuous intergration (CI) before git commit using pre-commit.
[0.2.1] - 2023-05-01¶
Fixed¶
Fix map projection dependency by replacing geopandas with basemap.
[0.2.0] - 2023-04-19¶
Added¶
Manual hyper parameters selection and automated hyper parameter selection using FLAML and Ray for every existed models
New Models:
Classification Models
Multi-layer Perceptron
Extra Trees
[0.1.0] - 2023-02-01¶
Added¶
End-to-end cutomized automated machine learning training pipeline with specialized design pattern to achieve the MLOps core of continuous training in command line interface.
New Models
Regression Models
Linear Regression
Polynomial Regression
Decision Tree
Extra Trees
Random Forest
XGBoost
Support Vector Machine
Multi-layer Perceptron
Classification Models
Decision Tree
Random Forest
XGBoost
Support Vector Machine
Logistic Regression
Clustering Models
KMeans
DBSCAN
Decomposition Models
Principle Component Analysis
Build up continuous integration (CI) after git commit using Git Action