Run2 parallel orchestration

Parallel Orchestration for Hyperparameter Search

A structured way to iterate through multiple hyperparameters while optimizing for performance.

Strategy

1. Loop through shape_weight (most expensive since it changes the distance metric).
2. For each shape_weight, precompute distance matrices for all images.
3. Parallelize the evaluation of min_samples and epsilon for that shape_weight.

from joblib import Parallel, delayed

def process_weight(w, citsci_groups, expert_groups, all_images, ms_range, eps_range):
    # Precompute dist matrices
    dist_matrices = {img: precompute_distances(citsci_groups[img], w) 
                     for img in citsci_groups if img in expert_groups}
    
    results = []
    for ms in ms_range:
        for eps in eps_range:
            f1s, deltas = [], []
            for img in all_images:
                # ... evaluation logic ...
                f1s.append(f1)
                if not np.isnan(delta): deltas.append(delta)
            results.append({'F1': np.mean(f1s), 'delta': np.mean(deltas), ...})
    return results