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Running on Zero
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9d24374 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | from __future__ import annotations
import argparse
import csv
import json
from collections import defaultdict
from pathlib import Path
import numpy as np
import scipy.sparse as sp
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score, precision_recall_curve, roc_auc_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import LabelEncoder, StandardScaler
from experiments.common import ARTIFACT_DIR
from experiments.split import grouped_concept_split
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description='Evaluate predictive SAE features and residual probes.')
parser.add_argument('--activation-dir', type=Path, default=ARTIFACT_DIR / 'activations')
parser.add_argument('--output-dir', type=Path, default=ARTIFACT_DIR)
parser.add_argument('--top-features', type=int, default=20)
parser.add_argument('--min-train-fires', type=int, default=3)
parser.add_argument('--seed', type=int, default=42)
return parser.parse_args()
def _best_threshold(y_true: np.ndarray, scores: np.ndarray) -> float:
precision, recall, thresholds = precision_recall_curve(y_true, scores)
if thresholds.size == 0:
return 0.0
denom = precision[:-1] + recall[:-1]
f1 = np.divide(
2 * precision[:-1] * recall[:-1],
denom,
out=np.zeros_like(denom),
where=denom > 0,
)
return float(thresholds[int(np.argmax(f1))])
def _evaluate_feature(
train_scores: np.ndarray,
test_scores: np.ndarray,
y_train: np.ndarray,
y_test: np.ndarray,
) -> tuple[float, float, float]:
threshold = _best_threshold(y_train, train_scores)
train_auc = float(roc_auc_score(y_train, train_scores))
test_auc = float(roc_auc_score(y_test, test_scores))
pred = (test_scores >= threshold).astype(int)
f1 = float(f1_score(y_test, pred, zero_division=0))
return train_auc, test_auc, f1, threshold
def _sparse_cosine(a: sp.csr_matrix, b: sp.csr_matrix) -> float:
numerator = float(a.multiply(b).sum())
denom = float(np.sqrt(a.multiply(a).sum()) * np.sqrt(b.multiply(b).sum()))
return numerator / denom if denom > 0 else 1.0
def _jaccard(a: sp.csr_matrix, b: sp.csr_matrix) -> float:
sa = set(a.indices.tolist())
sb = set(b.indices.tolist())
union = sa | sb
return len(sa & sb) / len(union) if union else 1.0
def main() -> None:
args = parse_args()
args.output_dir.mkdir(parents=True, exist_ok=True)
metadata = json.loads((args.activation_dir / 'metadata.json').read_text(encoding='utf-8'))
rows = metadata['rows']
layers = [int(x) for x in metadata['layers']]
train_idx, test_idx = grouped_concept_split(rows, seed=args.seed)
labels = np.array([row['concept'] for row in rows])
concepts = sorted(set(labels.tolist()))
feature_rows: list[dict] = []
layer_rows: list[dict] = []
stability_rows: list[dict] = []
encoder = LabelEncoder().fit(labels)
y_all = encoder.transform(labels)
y_train_multi = y_all[train_idx]
y_test_multi = y_all[test_idx]
for layer in layers:
x = sp.load_npz(args.activation_dir / f'features_layer{layer}.npz').tocsr()
x_csc = x.tocsc()
residuals = np.load(args.activation_dir / f'residuals_layer{layer}.npy').astype(np.float32)
recon = json.loads(
(args.activation_dir / f'reconstruction_layer{layer}.json').read_text(encoding='utf-8')
)
probe = make_pipeline(
StandardScaler(),
LogisticRegression(max_iter=2500, class_weight='balanced', random_state=args.seed),
)
probe.fit(residuals[train_idx], y_train_multi)
pred = probe.predict(residuals[test_idx])
probs = probe.predict_proba(residuals[test_idx])
probe_f1 = float(f1_score(y_test_multi, pred, average='macro'))
probe_auc = float(
roc_auc_score(y_test_multi, probs, multi_class='ovr', average='macro')
)
layer_rows.append(
{
'layer': layer,
'linear_probe_macro_auroc': probe_auc,
'linear_probe_macro_f1': probe_f1,
'reconstruction_cosine': recon['mean_cosine'],
'reconstruction_nmse': recon['mean_nmse'],
'mean_active_features': recon['mean_active_features'],
}
)
train_matrix = x[train_idx]
candidate_ids, counts = np.unique(train_matrix.indices, return_counts=True)
candidate_ids = candidate_ids[counts >= args.min_train_fires]
for concept in concepts:
y_train = (labels[train_idx] == concept).astype(int)
y_test = (labels[test_idx] == concept).astype(int)
concept_results: list[dict] = []
for feature_id in candidate_ids.tolist():
train_scores = x_csc[train_idx, feature_id].toarray().ravel()
if int((train_scores > 0).sum()) < args.min_train_fires:
continue
test_scores = x_csc[test_idx, feature_id].toarray().ravel()
train_auc, test_auc, f1, threshold = _evaluate_feature(
train_scores, test_scores, y_train, y_test
)
pos_train = train_scores[y_train == 1]
neg_train = train_scores[y_train == 0]
result = {
'layer': layer,
'concept': concept,
'feature_id': int(feature_id),
'train_auroc': train_auc,
'auroc': test_auc,
'f1': f1,
'threshold': threshold,
'activation_rate_pos': float(np.mean(pos_train > 0)),
'activation_rate_neg': float(np.mean(neg_train > 0)),
'mean_activation_pos': float(np.mean(pos_train)),
'mean_activation_neg': float(np.mean(neg_train)),
}
concept_results.append(result)
concept_results.sort(
key=lambda item: (item['train_auroc'], item['activation_rate_pos'] - item['activation_rate_neg']),
reverse=True,
)
feature_rows.extend(concept_results[: args.top_features])
pair_map: dict[str, list[int]] = defaultdict(list)
for idx, row in enumerate(rows):
pair_map[row['pair_id']].append(idx)
for pair_id, indices in pair_map.items():
if len(indices) != 2:
continue
a, b = indices
stability_rows.append(
{
'layer': layer,
'pair_id': pair_id,
'concept': rows[a]['concept'],
'topk_jaccard': _jaccard(x.getrow(a), x.getrow(b)),
'sparse_cosine': _sparse_cosine(x.getrow(a), x.getrow(b)),
}
)
print(f'Evaluated layer {layer}', flush=True)
with (args.output_dir / 'feature_catalog.csv').open('w', newline='', encoding='utf-8') as handle:
writer = csv.DictWriter(handle, fieldnames=list(feature_rows[0].keys()))
writer.writeheader()
writer.writerows(feature_rows)
with (args.output_dir / 'layer_metrics.csv').open('w', newline='', encoding='utf-8') as handle:
writer = csv.DictWriter(handle, fieldnames=list(layer_rows[0].keys()))
writer.writeheader()
writer.writerows(layer_rows)
with (args.output_dir / 'stability.csv').open('w', newline='', encoding='utf-8') as handle:
writer = csv.DictWriter(handle, fieldnames=list(stability_rows[0].keys()))
writer.writeheader()
writer.writerows(stability_rows)
split_payload = {
'seed': args.seed,
'train_indices': train_idx,
'test_indices': test_idx,
'n_train': len(train_idx),
'n_test': len(test_idx),
}
(args.output_dir / 'split.json').write_text(json.dumps(split_payload, indent=2), encoding='utf-8')
print(f'Wrote evaluation artifacts to {args.output_dir}')
if __name__ == '__main__':
main()
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