---
tags: 
aliases: 
date: 2025-03-02 
time: 20:53:47
description: 
---

# Project Structure
```
mlpredictor/  
│  
├── mlpredictor/  
│   ├── __init__.py  
│   ├── model.py  
│  
├── tests/  
│   ├── test_model.py  
│  
├── LICENSE  
├── README.md  
├── pyproject.toml  
└── .gitignore
```


## Content of `setup.py`
```
from setuptools import setup  
  
setup(  
name='mypackage',  
version='0.1',  
packages=['mypackage'],  
install_requires=['requests'])
```

## Content of `pyproject.toml`
```toml
[build-system]  
requires = ["setuptools>=42", "wheel"]  
build-backend = "setuptools.build_meta"  
  
[project]  
name = "mlpredictor"  
version = "0.1.0"  
description = "A simple machine learning package using scikit-learn"  
authors = [  
{name = "Ebrahim", email = "ebimsv0501@gmail.com"}  
]  
license = {text = "MIT"}  
readme = "README.md"  
requires-python = ">=3.6"  
dependencies = [  
"scikit-learn>=1.0",  
]  
  
[project.urls]  
"Homepage" = "https://github.com/ebimsv/mlpredictor"
```
- **[build-system]**: Specifies the build system requirements (i.e., using `setuptools` and `wheel`).
- **[project]**: Contains metadata about the package, like name, version, description, and dependencies.


## Content of `README.md`
<pre><code>
# MLPredictor  
  
MLPredictor is a simple machine learning package that trains a RandomForest model using the Iris dataset and enables users to make predictions. The package is built using `scikit-learn` and is intended as a demonstration of packaging Python machine learning projects for distribution.  
  
## Features  
  
- Train a RandomForestClassifier on the Iris dataset.  
- Make predictions on new data after training.  
- Save and load trained models.  
  
## Installation  
  
You can install the package via **PyPI** or from **source**.  
  
### Install from PyPI  
  
```bash  
pip install mlpredictor  
```  
  
### Install from Source (GitHub)  
  
```bash  
git clone https://github.com/ebimsv/mlpredictor.git  
cd mlpredictor  
pip install .  
```  
  
## Usage  
  
After installation, you can use `MLPredictor` to train a model and make predictions.  
  
### Example: Training and Making Predictions  
  
```python  
from mlpredictor import MLPredictor  
  
# Initialize the predictor  
predictor = MLPredictor()  
  
# Train the model on the Iris dataset  
predictor.train()  
  
# Make a prediction on a sample input  
sample_input = [5.1, 3.5, 1.4, 0.2]  
prediction = predictor.predict(sample_input)  
  
print(f"Predicted class: {prediction}")  
```
</code></pre>

## Content of `.gitignore`
```
*.pyc  
__pycache__/  
*.pkl  
dist/  
build/
```


# Test
## Content of `tests/test_model.py`
```python
import pytest  
from mlpredictor import MLPredictor  
  
def test_train_and_predict():  
    model = MLPredictor()  
    model.train()  
    result = model.predict([5.1, 3.5, 1.4, 0.2])  
    assert len(result) == 1  
  
if __name__ == "__main__":  
    pytest.main()
```

## Run test
```bash
pytest tests
```


# Install
## Test locally
```
pip install .
```

## Publish on PyPI
1. **Install 'Twine' and 'build'**:
```
pip install twine build
```

2. **Build the Package**:
```
python -m build
```

3. **Upload to PyPI**
```
twine upload dist/*
```

# 參考來源
- [Building Python Packages. A Comprehensive Guide to setup.py and… | by Ebrahim Mousavi | Medium](https://medium.com/@ebimsv/building-python-packages-07fbfbb959a9)