https://github.com/yangfa-zhang/lunax
Lunax is a machine learning framework specifically designed for the processing and analysis of tabular data.
https://github.com/yangfa-zhang/lunax
data-analysis data-science lunax machine-learning tabular-data
Last synced: about 1 month ago
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Lunax is a machine learning framework specifically designed for the processing and analysis of tabular data.
- Host: GitHub
- URL: https://github.com/yangfa-zhang/lunax
- Owner: yangfa-zhang
- License: other
- Created: 2025-05-03T06:50:37.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-06-17T03:13:57.000Z (7 months ago)
- Last Synced: 2025-10-27T16:46:21.167Z (3 months ago)
- Topics: data-analysis, data-science, lunax, machine-learning, tabular-data
- Language: Python
- Homepage: https://lunax-doc.readthedocs.io/en/latest/
- Size: 25.8 MB
- Stars: 12
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.CN.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://pypi.org/project/lunax/)
###
[中文](README.CN.md) | [EN](README.md)
###
``lunax`` 是一个用于表格数据处理分析的机器学习框架。 lunax这个名字来自于图中的这只可爱的小猫🐱，是华南理工大学最受欢迎的小猫**luna**。在[API文档](https://lunax-doc.readthedocs.io/en/latest/)中查看更详细的说明**⭐️ 如果喜欢，欢迎点个star！ ⭐️**
---
### 如何下载
```bash
conda create -n 你的环境名 python=3.11
conda activate 你的环境名
pip install lunax
```
### 已有功能
- 数据加载和预处理
- EDA分析
- 自动化机器学习建模
- 模型评估和解释
- 集成学习
- 特征重要性分析
- 面向对象设计，统一接口便于扩展
- 使用pytest进行单元测试，保证代码质量
### 快速开始
#### 数据加载和预处理
```Python
from lunax.data_processing import *
df_train = load_data('train.csv') # 或者 df = load_data('train.parquet')
target = '标签列名'
df_train = preprocess_data(df_train,target) # 数据预处理, 包括缺失值处理, 特征编码, 特征缩放
X_train, X_val, y_train, y_val = split_data(df_train, target)
```
#### EDA分析
```Python
from lunax.viz import numeric_eda, categoric_eda
numeric_eda([df_train,df_test],['train','test'],target=target) # 数值型特征分析
categoric_eda([df_train,df_test],['train','test'],target=target) # 类别型特征分析
```
#### 自动化机器学习建模
```Python
from lunax.models import xgb_clf # 或者 xgb_reg, lgbm_reg, lgbm_clf, cat_reg, cat_clf
from lunax.hyper_opt import OptunaTuner
tuner = OptunaTuner(n_trials=10,model_class="XGBClassifier") # 超参数优化, n_trials为优化次数
# 或者 "XGBRegressor", "LGBMRegressor", "LGBMClassifier", "CatRegressor", "CatClassifier"
results = tuner.optimize(X_train, y_train, X_val, y_val)
best_params = results['best_params']
model = xgb_clf(best_params)
model.fit(X_train, y_train)
```
#### 模型评估和解释
```Python
model.evaluate(X_val, y_val)
```
```text
[lunax]> label information:
+---------+---------+
| label | count |
+=========+=========+
| 1 | 319 |
+---------+---------+
| 0 | 119 |
+---------+---------+
[lunax]> model evaluation results:
+-----------+------------+-------------+----------+------+
| metrics | accuracy | precision | recall | f1 |
+===========+============+=============+==========+======+
| values | 0.73 | 0.53 | 0.73 | 0.61 |
+-----------+------------+-------------+----------+------+
```
#### 集成学习
```Python
from lunax.ensembles import HillClimbingEnsemble
model1 = xgb_clf()
model2 = lgbm_clf()
model3 = cat_clf()
for model in [model1, model2, model3]:
model.fit(X_train, y_train)
ensemble = HillClimbingEnsemble(
models=[model1, model2, model3],
metric=['auc'],
maximize=True
)
best_weights = ensemble.fit(X_val, y_val)
predictions = ensemble.predict(df_test)
```
#### 特征重要性分析
```Python
from lunax.xai import TreeExplainer
explainer = TreeExplainer(model)
explainer.plot_summary(X_val)
importance = explainer.get_feature_importance(X_val)
```
```text
[lunax]> Clear blue/red separation indicates a highly influential feature.
```
```text
[lunax]> Feature Importance Ranking:
+----+---------------+---------------------+
| | Feature | Importance |
+----+---------------+---------------------+
| 1 | cloud | 2.3085615634918213 |
| 2 | sunshine | 0.6377484202384949 |
| 3 | dewpoint | 0.5257667899131775 |
| 4 | humidity | 0.4827548861503601 |
| 5 | windspeed | 0.40086665749549866 |
| 6 | id | 0.38620123267173767 |
| 7 | pressure | 0.3780971169471741 |
| 8 | mintemp | 0.32988569140434265 |
| 9 | day | 0.30587586760520935 |
| 10 | maxtemp | 0.26082852482795715 |
| 11 | winddirection | 0.23236176371574402 |
| 12 | temparature | 0.17218443751335144 |
+----+---------------+---------------------+
```
#### 预测
```Python
df_test = load_data('test.csv')
df_test = preprocess_data(df_train,target)
y_pred = model.predict(df_test)
# y_pred_proba = model.predict_proba(X_test)
```