1import json
 2import numpy as np
 3from experiment import make_data, graph_from_x, cut_ratio, train_once, SEED
 4
 5def run(seed):
 6    X, y = make_data(seed)
 7    rng = np.random.RandomState(seed + 101)
 8    policies = [X, X + rng.randn(*X.shape).astype('float32') * 0.18,
 9                X[rng.permutation(len(X))]]
10    graphs = [graph_from_x(z, k=10, sigma=0.8) for z in policies]
11    cuts = [cut_ratio(W, y) for W in graphs]
12    uniform = train_once('uniform', X, y, graphs, cuts, seed + 17)
13    cut = train_once('cut', X, y, graphs, cuts, seed + 17)
14    return {
15        'seed': seed, 'cuts': cuts,
16        'uniform_accuracy': uniform['accuracy'],
17        'cut_accuracy': cut['accuracy'],
18        'uniform_pred_cut': uniform['pred_cut_uniform_graph'],
19        'cut_pred_cut': cut['pred_cut_uniform_graph'],
20        'cut_q': cut['q']
21    }
22
23rows = [run(s) for s in [SEED, SEED + 1, SEED + 2]]
24out = {'runs': rows}
25for key in ['uniform_accuracy', 'cut_accuracy', 'uniform_pred_cut', 'cut_pred_cut']:
26    values = np.array([r[key] for r in rows])
27    out[key + '_mean'] = float(values.mean())
28    out[key + '_std'] = float(values.std())
29print(json.dumps(out, indent=2))
30with open('robustness.json', 'w') as f:
31    json.dump(out, f, indent=2)