Implements an automated ensemble search using hill climbing and grid search on rank-transformed out-of-fold predictions. Takes a set of candidate model predictions and searches for optimal ensemble weights to maximize a target metric (e.g., ROC-AUC). The algorithm applies rank transformation (percentile) before blending, which normalizes prediction scales. The resulting ensemble checkpoint can be serialized via `build_checkpoint.py`. This component is highly reusable for any supervised learning task where OOF predictions are available, requiring only numpy arrays and a metric function.

Computes a comprehensive set of structural graph features for nodes in a biological network (e.g., degree, clustering coefficient, eigenvector centrality, PageRank, betweenness). These features are used as input to a graph neural network for node classification. The script relies only on networkx and numpy, making it easily extractable and applicable to any undirected graph dataset. Demonstrates non-obvious skill in combining classical graph metrics with modern GNNs.

Defines a neural network architecture for anomalous diffusion OOD detection that ingests physics-inspired features (e.g., mean squared displacement, moment ratios, autocorrelation) from particle trajectories. The model uses these features to predict continuous anomalous diffusion exponents and detect out-of-distribution trajectories. The feature extraction logic is parameterized and modular, allowing reuse for any trajectory-based anomaly detection task.