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import argparse
import csv
import json
from collections import defaultdict
from pathlib import Path
import numpy as np
import scipy.sparse as sp
from experiments.common import ARTIFACT_DIR
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description='Estimate prompt-wide candidate selection stability from saved SAE activations.'
)
parser.add_argument('--activation-dir', type=Path, default=ARTIFACT_DIR / 'activations')
parser.add_argument('--output', type=Path, default=ARTIFACT_DIR / 'selection_stability.csv')
parser.add_argument('--resamples', type=int, default=128)
parser.add_argument('--top-features', type=int, default=20)
parser.add_argument('--seed', type=int, default=42)
return parser.parse_args()
def _vector_mean(matrix: sp.csr_matrix, indices: np.ndarray) -> np.ndarray:
if indices.size == 0:
return np.zeros(matrix.shape[1], dtype=np.float32)
return np.asarray(matrix[indices].mean(axis=0)).ravel().astype(np.float32, copy=False)
def _vector_rate(binary: sp.csr_matrix, indices: np.ndarray) -> np.ndarray:
if indices.size == 0:
return np.zeros(binary.shape[1], dtype=np.float32)
return np.asarray(binary[indices].mean(axis=0)).ravel().astype(np.float32, copy=False)
def balanced_candidate_score(
target_mean: np.ndarray,
other_mean: np.ndarray,
target_rate: np.ndarray,
) -> np.ndarray:
"""Live-compatible selectivity × coverage × log-magnitude candidate score."""
target = np.asarray(target_mean, dtype=np.float64)
other = np.asarray(other_mean, dtype=np.float64)
rate = np.asarray(target_rate, dtype=np.float64)
difference = target - other
denom = np.abs(target) + np.abs(other) + 1e-12
selectivity = np.where(difference > 0.0, difference / denom, 0.0)
return selectivity * np.clip(rate, 0.0, 1.0) * np.log1p(np.maximum(target, 0.0))
def _rank_top(scores: np.ndarray, k: int) -> tuple[np.ndarray, np.ndarray]:
positive = np.flatnonzero(scores > 0.0)
if positive.size == 0:
return np.empty(0, dtype=int), np.empty(0, dtype=float)
k = min(int(k), int(positive.size))
candidate_scores = scores[positive]
local = np.argpartition(candidate_scores, -k)[-k:]
ids = positive[local]
ordered = np.argsort(scores[ids])[::-1]
ids = ids[ordered]
return ids.astype(int), scores[ids].astype(float)
def _sample_indices(
concept_indices: dict[str, np.ndarray],
rng: np.random.Generator,
) -> dict[str, np.ndarray]:
return {
concept: rng.choice(indices, size=indices.size, replace=True)
for concept, indices in concept_indices.items()
}
def _concept_statistics(
matrix: sp.csr_matrix,
binary: sp.csr_matrix,
concept_indices: dict[str, np.ndarray],
) -> tuple[dict[str, np.ndarray], dict[str, np.ndarray]]:
means = {
concept: _vector_mean(matrix, indices)
for concept, indices in concept_indices.items()
}
rates = {
concept: _vector_rate(binary, indices)
for concept, indices in concept_indices.items()
}
return means, rates
def _score_for_concept(
concept: str,
means: dict[str, np.ndarray],
rates: dict[str, np.ndarray],
) -> np.ndarray:
other_concepts = [name for name in means if name != concept]
other_mean = np.mean(np.stack([means[name] for name in other_concepts]), axis=0)
return balanced_candidate_score(means[concept], other_mean, rates[concept])
def main() -> None:
args = parse_args()
if args.resamples < 1:
raise ValueError('--resamples must be at least 1.')
if args.top_features < 1:
raise ValueError('--top-features must be at least 1.')
metadata = json.loads((args.activation_dir / 'metadata.json').read_text(encoding='utf-8'))
pooling = metadata.get('feature_pooling', '')
if 'prompt-wide' not in pooling:
raise RuntimeError(
'Selection stability requires v0.14 prompt-wide activation artifacts. '
'Rerun experiments.collect_activations before this analysis.'
)
rows = metadata['rows']
layers = [int(layer) for layer in metadata['layers']]
concepts = sorted({row['concept'] for row in rows})
concept_indices = {
concept: np.array(
[idx for idx, row in enumerate(rows) if row['concept'] == concept],
dtype=int,
)
for concept in concepts
}
rng = np.random.default_rng(args.seed)
output_rows: list[dict] = []
for layer in layers:
matrix = sp.load_npz(args.activation_dir / f'features_layer{layer}.npz').tocsr()
binary = matrix.copy()
binary.data = np.ones_like(binary.data, dtype=np.float32)
full_means, full_rates = _concept_statistics(matrix, binary, concept_indices)
full_scores = {
concept: _score_for_concept(concept, full_means, full_rates)
for concept in concepts
}
full_orders: dict[str, np.ndarray] = {
concept: np.argsort(scores)[::-1]
for concept, scores in full_scores.items()
}
support: dict[tuple[str, int], int] = defaultdict(int)
ranks: dict[tuple[str, int], list[int]] = defaultdict(list)
for _ in range(args.resamples):
sampled = _sample_indices(concept_indices, rng)
means, rates = _concept_statistics(matrix, binary, sampled)
for concept in concepts:
scores = _score_for_concept(concept, means, rates)
ids, _ = _rank_top(scores, args.top_features)
for rank, feature_id in enumerate(ids.tolist(), start=1):
key = (concept, int(feature_id))
support[key] += 1
ranks[key].append(rank)
for concept in concepts:
full_rank_map = np.empty(matrix.shape[1], dtype=np.int32)
full_rank_map[full_orders[concept]] = np.arange(1, matrix.shape[1] + 1, dtype=np.int32)
seen = {
feature_id
for (seen_concept, feature_id), count in support.items()
if seen_concept == concept and count > 0
}
full_ids, _ = _rank_top(full_scores[concept], max(args.top_features, 50))
seen.update(int(feature_id) for feature_id in full_ids.tolist())
for feature_id in seen:
key = (concept, feature_id)
feature_ranks = ranks.get(key, [])
output_rows.append(
{
'layer': layer,
'concept': concept,
'feature_id': feature_id,
'full_score': float(full_scores[concept][feature_id]),
'full_rank': int(full_rank_map[feature_id]),
'resample_support': float(support.get(key, 0) / args.resamples),
'median_resample_rank': (
float(np.median(feature_ranks)) if feature_ranks else float('nan')
),
'mean_resample_rank': (
float(np.mean(feature_ranks)) if feature_ranks else float('nan')
),
'resamples': int(args.resamples),
'top_features_per_resample': int(args.top_features),
}
)
print(f'Stability analysis complete for layer {layer}', flush=True)
args.output.parent.mkdir(parents=True, exist_ok=True)
fieldnames = [
'layer',
'concept',
'feature_id',
'full_score',
'full_rank',
'resample_support',
'median_resample_rank',
'mean_resample_rank',
'resamples',
'top_features_per_resample',
]
with args.output.open('w', newline='', encoding='utf-8') as handle:
writer = csv.DictWriter(handle, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(output_rows)
print(f'Wrote {len(output_rows)} selection-stability rows to {args.output}')
if __name__ == '__main__':
main()
|