vault backup: 2025-03-04 11:12:06

-20.01. Programming/Python/Write a Package.md

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+---
+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)