evaluate_sklearn.py

import io
import json
import numpy as np
import sys
import os
from datasets import load_dataset
from hoho2025.metric_helper import hss
import sklearn_submission

print("Loading dataset...")
dataset = load_dataset('usm3d/hoho22k_2026_trainval', split='train', streaming=True, trust_remote_code=True)

samples = []
for idx, s in enumerate(dataset):
    if idx >= 10:
        break
    samples.append(s)

scores = []
for idx, sample in enumerate(samples):
    print(f"Testing sample {idx}")
    try:
        pred_v, pred_e = sklearn_submission.predict_wireframe_sklearn(sample)
    except Exception as e:
        print(f"Error on sample {idx}: {e}")
        pred_v, pred_e = np.zeros((2, 3)), [(0, 1)]
    
    gt_v = sample.get('wf_vertices')
    gt_e = sample.get('wf_edges')
    
    if gt_v is None or gt_e is None:
        print(f"Skipping sample {idx} due to missing ground truth.")
        continue

    res = hss(pred_v, pred_e, gt_v, gt_e)
    scores.append(res.hss)
    print(f"Sample {idx} HSS: {res.hss:.4f}")

if scores:
    print(f"Average HSS: {sum(scores) / len(scores):.4f}")
else:
    print("No valid scores.")