thinkml 1.0.1

End-to-End Machine Learning Workflow Library

ThinkML

ThinkML is a comprehensive machine learning library built on top of scikit-learn, providing advanced validation, feature engineering, and model selection capabilities with robust edge case handling.

IMPORTANT: This library is exclusively available from the author's GitHub account. It is not and will not be available on PyPI or any other package repository. The only official source is: https://github.com/ArpanChaudhary/ThinkML

Features

1. Advanced Validation Methods

• Nested Cross-Validation with enhanced error handling
• Time Series Validation with gap support
• Stratified Group Validation
• Bootstrap Validation with stratification

2. Feature Engineering

• Automated feature creation
• Intelligent feature selection
• Support for various feature types:
  • Polynomial features
  • Interaction features
  • Domain-specific features

3. Robust Preprocessing

• Advanced scaling with edge case handling
• Support for:
  • Empty datasets
  • Single row datasets
  • Missing data
  • Extreme values
  • Highly correlated features

4. Performance

• Efficient handling of large datasets (1M+ rows)
• Memory-optimized validation methods
• Parallel processing support
• Chunked data processing

Installation

ThinkML is exclusively available from the author's GitHub repository. It is not available on PyPI or any other package repository. Here are the only official installation methods:

Method 1 (Recommended)

pip install git+https://github.com/ArpanChaudhary/ThinkML.git

Method 2

# Clone the repository
git clone https://github.com/ArpanChaudhary/ThinkML.git

# Change to the project directory
cd ThinkML

# Install the package
pip install -e .

Method 3

# Download the ZIP file from https://github.com/ArpanChaudhary/ThinkML
# Extract the ZIP file
# Navigate to the extracted directory
cd ThinkML

# Install the package
pip install -e .

Note: Any other installation method or source is not official and may contain unauthorized modifications. Always install from the official GitHub repository: https://github.com/ArpanChaudhary/ThinkML

Dependencies

Before installing ThinkML, ensure you have the following dependencies:

• Python 3.6+
• scikit-learn >= 0.24.0
• numpy >= 1.19.0
• pandas >= 1.2.0

You can install these dependencies automatically during ThinkML installation, or manually:

pip install scikit-learn>=0.24.0 numpy>=1.19.0 pandas>=1.2.0

Quick Start

from thinkml.validation import NestedCrossValidator
from thinkml.feature_engineering import create_features
from sklearn.ensemble import RandomForestClassifier

# Initialize validator
validator = NestedCrossValidator(
    estimator=RandomForestClassifier(),
    param_grid={'n_estimators': [100, 200]},
    inner_cv=3,
    outer_cv=5
)

# Create features and validate
X_new = create_features(X)
results = validator.fit_predict(X_new, y)

print(f"Mean Score: {results['mean_score']:.3f} ± {results['std_score']:.3f}")

Documentation

For detailed documentation, visit our documentation site:

• Implementation Details
• User Guide
• API Reference

Requirements

• Python 3.6+
• scikit-learn >= 0.24.0
• numpy >= 1.19.0
• pandas >= 1.2.0

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

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

Citation

If you use ThinkML in your research, please cite:

@software{thinkml2025,
  title = {ThinkML: Advanced Validation and Feature Engineering for Machine Learning},
  author = {Chaudhary, Arpan},
  year = {2025},
  version = {1.0},
  url = {https://github.com/ArpanChaudhary/ThinkML}
}

Acknowledgments

• Thanks to all contributors who have helped shape ThinkML
• Inspired by various open-source machine learning libraries
• Special thanks to the Python data science community

Contact

Arpan Chaudhary - @ArpanChaudhary

Project Link: https://github.com/ArpanChaudhary/ThinkML

Support

If you encounter any issues or have questions, please:

1. Check the documentation
2. Search existing issues
3. Create a new issue if needed

Roadmap

• Enhanced deep learning support
• Automated hyperparameter tuning
• Distributed computing support
• Model deployment tools
• Web API interface
• GUI for interactive model building