onithrasML

A Python machine learning library with a Python-friendly API and a C++ backend for performance-oriented computation.

onithrasML is an open-source machine learning library for Python, designed to provide simple and efficient implementations of common machine learning algorithms, preprocessing utilities, and model-selection tools.

✨ Features

• 📊 Data imputation utilities
• 📐 Feature scaling utilities
• 📈 Linear Regression
• 🎯 Logistic Regression
• 🌳 Decision Tree
• 🔍 Model selection utilities
• ⚡ C++ backend for performance-critical operations
• 🐍 Simple Python API
• 🔌 Easy integration into machine learning projects

---

📦 Installation

Install the latest published version from PyPI:

pip install onithrasml

Or:

python -m pip install onithrasml

Upgrade

python -m pip install --upgrade onithrasml

Install a specific version

python -m pip install onithrasml==0.1.0

Verify Installation

python -c "import onithrasML; print(onithrasML.__file__)"

You can also check the installed package:

python -m pip show onithrasml

---

🚀 Quick Start

After installation, import onithrasML in Python:

import onithrasML

print(onithrasML)

You can also verify the installation directly from the terminal:

python -c "import onithrasML; print('onithrasML installed successfully')"

---

🤖 Machine Learning Models

Linear Regression

from onithrasML.linear_model import LinearRegression

model = LinearRegression()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print(predictions)

Logistic Regression

from onithrasML.linear_model import LogisticRegression

model = LogisticRegression()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print(predictions)

Example with NumPy

import numpy as np

from onithrasML.linear_model import LinearRegression

X_train = np.array([
    [1.0],
    [2.0],
    [3.0],
    [4.0]
])

y_train = np.array([
    2.0,
    4.0,
    6.0,
    8.0
])

X_test = np.array([
    [5.0],
    [6.0]
])

model = LinearRegression()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print(predictions)

Note: The exact classes, constructor arguments, and methods available depend on the installed version of onithrasML.

---

🧩 Imputation

The imputer module provides utilities for handling missing values in datasets.

Example:

from onithrasML import imputer

If your installed version exposes a specific imputer class or function, import it from the module:

from onithrasML.imputer import YourImputer

---

🔍 Model Selection

The model_selection module provides utilities for selecting and evaluating machine learning models.

Example:

from onithrasML import model_selection

A specific utility can be imported according to the API exposed by your installed version:

from onithrasML.model_selection import YourUtility

---

⚡ C++ Backend

Performance-critical components are implemented in C++ and exposed to Python through bindings.

The C++ backend currently contains components related to:

• Linear Regression
• Logistic Regression
• Decision Tree

This allows computationally intensive operations to run using native compiled code while maintaining a Python-friendly API.

The C++ source code is maintained in the cpp/ directory during development.

---

📁 Project Structure

onithrasML/
├── src/
│   └── onithrasML/
│       ├── __init__.py
│       ├── _backend/
│       ├── imputer/
│       ├── linear_model/
│       └── model_selection/
│
├── cpp/
│   ├── bindings.cpp
│   ├── decision_tree.cpp
│   ├── linear_regression.cpp
│   └── logistic_regression.cpp
│
├── tests/
├── benchmarks/
├── README.md
├── LICENSE
└── pyproject.toml

---

🛠️ Development Setup

1. Clone the Repository

git clone https://github.com/vishwa-Ansh/onithrasML.git

cd onithrasML

2. Create a Virtual Environment

python -m venv .venv

3. Activate the Environment

macOS / Linux

source .venv/bin/activate

Windows PowerShell

.venv\Scripts\Activate.ps1

4. Install Development Tools

python -m pip install --upgrade pip

python -m pip install build twine pytest

5. Install the Project

Install it in editable mode:

python -m pip install -e .

---

🧪 Testing

Run the test suite:

pytest

Or:

python -m pytest

---

📦 Building the Package

Build the source distribution and wheel:

python -m build

The generated packages will appear inside the dist/ directory:

dist/
├── onithrasml-<version>.tar.gz
└── onithrasml-<version>-py3-none-any.whl

---

✅ Validate Before Publishing

Before uploading the package to PyPI, run:

python -m twine check dist/*

Both distributions should report:

PASSED

---

🚀 Publishing to PyPI

Before publishing a new release, update the version in pyproject.toml.

For example:

version = "0.1.1"

Remove Previous Build Files

rm -rf dist build src/*.egg-info

Build the New Release

python -m build

Validate the Release

python -m twine check dist/*

Upload to PyPI

python -m twine upload dist/*

When Twine asks for credentials, use:

Username: __token__
Password: <your PyPI API token>

🔐 Security: Never commit your PyPI API token to Git or put it inside your source code.

---

🔄 Release Workflow

Make changes
      ↓
Update tests and documentation
      ↓
Increment package version
      ↓
Remove old build files
      ↓
Build package
      ↓
Run twine check
      ↓
Upload to PyPI
      ↓
Install and verify

Typical release commands:

rm -rf dist build src/*.egg-info

python -m build

python -m twine check dist/*

python -m twine upload dist/*

---

🐛 Troubleshooting

ModuleNotFoundError: No module named 'onithrasML'

Make sure the package is installed in the same Python environment:

python -m pip install --upgrade onithrasml

Then verify:

python -c "import onithrasML; print(onithrasML.__file__)"

zsh: command not found: import

import is Python syntax, not a terminal command.

Start Python:

python

Then:

import onithrasML

Or use:

python -c "import onithrasML"

PyPI Returns HTTP 400 Bad Request

A common reason is trying to upload a version that already exists on PyPI.

Update the version in pyproject.toml:

version = "0.1.1"

Then rebuild and upload:

rm -rf dist build src/*.egg-info

python -m build

python -m twine check dist/*

python -m twine upload dist/*

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🤝 Contributing

Contributions, bug reports, feature requests, documentation improvements, and performance improvements are welcome.

How to Contribute

1. Fork the repository.
2. Create a new branch.
3. Make your changes.
4. Add or update tests.
5. Run the test suite.
6. Commit your changes.
7. Push your branch.
8. Open a pull request.

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📄 License

This project is open source.

See the LICENSE file for the license terms.

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👨‍💻 Author

Ansh Vishwakarma

GitHub: vishwa-Ansh

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📌 Project

onithrasML — Machine learning library for Python with a C++ backend.