hon-gyu / chameleon_cluster

A chameleon clustering algorithm implementation in PYTHON3.

Hi there 👋

🔍 I'm Hongyu, a passionate quant particularly interested in emerging machine learning applications.

🌍 Based in London. Open to work.

🗣️ Fluent in Mandarin & English.

Skills

• Programming Language: Python, R, SQL
• Machine Learning
  • Frameworks: PyTorch, Scikit-Learn, Keras
  • Deep Learning: LSTM
  • Model Interpretation: Shap, LIME
  • Clustering: Hierarchical Clustering, Chameleon Clustering, Semi-supervised Clustering
  • Ensemble Trees: Gradient-Boosted Trees, Random Forest
  • Conformal Prediction: MAPIE
  • Reinforcement Learning
• Others: Git, Linux, LaTex

Projects

• Semi-supervised Chameleon Clustering: An implementation of semi-supervised Chameleon clustering, capable of integrating must-link and cannot-link constraints at various levels of hierarchy to guide the clustering process.
• Model Fingerprint: A model-agnostic method to decompose predictions into linear, nonlinear and pairwise interaction effects. It can be helpful in feature selection and model interpretation.
• Using LSTM Model for Meta-labeling: An implementation that applies meta-labeling to minute-frequency stock data, utilizing LSTM as the primary model for price direction prediction, which forms the basis for a trading strategy augmented by a secondary meta-labeling layer to filter false positives and improve risk-return metrics.
• Semi-supervised Hedge Fund Clustering: A semi-supervised clustering method utilizing tree distance for enhanced hierarchical classification of funds in fund of funds analysis.
• Hierarchical Tree Distance: An implementation of the AKB tree distance, a measure designed to quantify the similarity between classes within a hierarchical label tree. Adept at emphasizing the importance of higher hierarchy errors, utilizing the taxonomy's inherent structure instead of simply flattening the hierarchy in traditional.

Micro Projects

• PyTorch Model Interpretation by Shap: An implemention of using PyTorch model in shap framework, which is a game theoretic approach to explain the output of any machine learning model. Created shap.Explanation object for PyTorch models to facilitate visualisation using a unified interface.