semiq-ml 0.1.2

A collection of reusable machine learning pipeline helpers

semiq-ml

A collection of reusable machine learning pipeline helpers designed to streamline ML workflows.

Description

ML Helper is a Python package that provides helper functions and classes to simplify common machine learning tasks, including baseline model training and hyperparameter tuning. It supports popular ML frameworks like LightGBM, XGBoost, and CatBoost.

Installation

From PyPI

You can install the package from PyPI using pip:

pip install semiq-ml

From Source

Install the package directly from GitHub:

pip install git+https://github.com/yourusername/semiq-ml.git

Or install from source:

git clone https://github.com/yourusername/semiq-ml.git
cd ml-helper
pip install -e .

Features

• Baseline Models: Quickly train baseline models with sensible defaults
• Preprocessing: Simple preprocessing steps for features (e.g., imputing, encoding, scaling)
• Feature Importance: Easy extraction of feature importance from trained models
• Integration: Seamless integration with scikit-learn, LightGBM, XGBoost, and CatBoost

Usage

Basic Example

from ml_helper import BaselineModel
import pandas as pd
from sklearn.model_selection import train_test_split

# Load dataset
data = pd.read_csv('your_data.csv')
X = data.drop('target', axis=1)
y = data['target']

model = BaselineModel()
model.fit(X, y)

model.get_results() # to get the results of the baseline model
lgbm = model.get_model('LGBM')

Documentation

For detailed documentation, please refer to the Wiki

Requirements

• Python >=3.12
• numpy
• pandas
• scikit-learn
• matplotlib
• seaborn
• lightgbm
• xgboost
• catboost

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.