File size: 8,146 Bytes
6cf9dac | 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 | """Quantify cross-seed stability of learned laboratory embeddings.
The comparison uses rank correlations between vectors of pairwise laboratory
distances. Pairwise distances are invariant to translation, rotation, and
reflection of an embedding, while Spearman correlation is also invariant to a
positive global rescaling. This avoids comparing arbitrary embedding axes.
"""
from __future__ import annotations
import argparse
import itertools
import json
from pathlib import Path
from typing import Any
import numpy as np
import pandas as pd
import torch
STRATEGIES = ("canonical_grouped", "scaffold_aware")
SEEDS = (123456, 123457, 123458)
MODEL_SPECS = {
"gat": ("graph_model.lab_embedding.weight", "gat_fold_*.pt"),
"gcn": ("graph_model.lab_embedding.weight", "gcn_fold_*.pt"),
"fpnn": ("lab_embedding.weight", "fp_nn_fold_*.pt"),
}
def _prepare_output_dir(path: Path) -> Path:
if path.exists() and any(path.iterdir()):
raise FileExistsError(f"Refusing nonempty output directory: {path}")
path.mkdir(parents=True, exist_ok=True)
return path
def _load_laboratory_order(neural_dir: Path) -> list[str]:
encoder_path = neural_dir / "fold_preprocessing" / "lab_encoder.json"
if not encoder_path.is_file():
raise FileNotFoundError(f"Incomplete checkpoint matrix: {encoder_path}")
payload = json.loads(encoder_path.read_text(encoding="utf-8"))
laboratories = payload.get("classes_in_index_order")
if not isinstance(laboratories, list) or len(laboratories) < 3:
raise ValueError(f"Invalid laboratory encoder: {encoder_path}")
if len(laboratories) != len(set(laboratories)):
raise ValueError(f"Duplicate laboratory labels: {encoder_path}")
return [str(value) for value in laboratories]
def _load_embedding(checkpoint_path: Path, key: str) -> np.ndarray:
payload: dict[str, Any] = torch.load(
checkpoint_path,
map_location="cpu",
weights_only=True,
)
model_state = payload.get("model_state")
if not isinstance(model_state, dict) or key not in model_state:
raise KeyError(f"Missing {key} in {checkpoint_path}")
embedding = model_state[key].detach().cpu().numpy().astype(float, copy=False)
if embedding.ndim != 2:
raise ValueError(f"Expected a two-dimensional embedding in {checkpoint_path}")
return embedding
def _mean_pairwise_distance_vector(
neural_dir: Path,
model: str,
expected_folds: int,
) -> tuple[tuple[str, ...], np.ndarray]:
key, pattern = MODEL_SPECS[model]
checkpoint_paths = sorted((neural_dir / "checkpoints" / model).glob(pattern))
if len(checkpoint_paths) != expected_folds:
raise FileNotFoundError(
"Incomplete checkpoint matrix: "
f"expected {expected_folds} {model} checkpoints in {neural_dir}, "
f"found {len(checkpoint_paths)}"
)
encoder_order = _load_laboratory_order(neural_dir)
common_order = tuple(sorted(encoder_order))
reorder = np.asarray([encoder_order.index(label) for label in common_order])
upper = np.triu_indices(len(common_order), k=1)
fold_distances: list[np.ndarray] = []
for checkpoint_path in checkpoint_paths:
embedding = _load_embedding(checkpoint_path, key)
if embedding.shape[0] != len(encoder_order):
raise ValueError(
f"Laboratory count mismatch in {checkpoint_path}: "
f"{embedding.shape[0]} versus {len(encoder_order)}"
)
aligned = embedding[reorder]
difference = aligned[:, None, :] - aligned[None, :, :]
distances = np.sqrt(np.sum(difference * difference, axis=2))
fold_distances.append(distances[upper])
return common_order, np.mean(np.vstack(fold_distances), axis=0)
def _spearman_correlation(vector_a: np.ndarray, vector_b: np.ndarray) -> float:
"""Compute Spearman rho without importing SciPy's additional OpenMP runtime."""
ranks_a = pd.Series(vector_a).rank(method="average").to_numpy(dtype=float)
ranks_b = pd.Series(vector_b).rank(method="average").to_numpy(dtype=float)
centered_a = ranks_a - ranks_a.mean()
centered_b = ranks_b - ranks_b.mean()
denominator = np.sqrt(
np.sum(centered_a * centered_a) * np.sum(centered_b * centered_b)
)
if denominator == 0:
raise ValueError("Cannot compute embedding stability from constant distances.")
return float(np.sum(centered_a * centered_b) / denominator)
def main() -> int:
parser = argparse.ArgumentParser(
description="Compare learned laboratory geometry across outer seeds."
)
parser.add_argument("--artifacts-root", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--expected-folds", type=int, default=6)
arguments = parser.parse_args()
artifacts_root = Path(arguments.artifacts_root).resolve()
output_dir = _prepare_output_dir(Path(arguments.output_dir).resolve())
distance_vectors: dict[tuple[str, str, int], np.ndarray] = {}
laboratory_orders: dict[tuple[str, str, int], tuple[str, ...]] = {}
for strategy in STRATEGIES:
for seed in SEEDS:
neural_dir = (
artifacts_root / strategy / f"seed_{seed}" / "neural_stack"
)
for model in MODEL_SPECS:
order, vector = _mean_pairwise_distance_vector(
neural_dir,
model,
arguments.expected_folds,
)
laboratory_orders[(strategy, model, seed)] = order
distance_vectors[(strategy, model, seed)] = vector
rows: list[dict[str, Any]] = []
for strategy in STRATEGIES:
for model in MODEL_SPECS:
for seed_a, seed_b in itertools.combinations(SEEDS, 2):
order_a = laboratory_orders[(strategy, model, seed_a)]
order_b = laboratory_orders[(strategy, model, seed_b)]
if order_a != order_b:
raise ValueError(
f"Laboratory labels differ for {strategy}/{model}: "
f"seed {seed_a} versus seed {seed_b}"
)
vector_a = distance_vectors[(strategy, model, seed_a)]
vector_b = distance_vectors[(strategy, model, seed_b)]
rho = _spearman_correlation(vector_a, vector_b)
rows.append(
{
"strategy": strategy,
"model": model,
"seed_a": seed_a,
"seed_b": seed_b,
"n_laboratories": len(order_a),
"n_laboratory_pairs": len(vector_a),
"spearman_rho": rho,
}
)
correlations = pd.DataFrame(rows)
correlations.to_csv(output_dir / "embedding_stability.csv", index=False)
aggregate = (
correlations.groupby(["strategy", "model"])["spearman_rho"]
.agg(["mean", "min", "max"])
.reset_index()
)
aggregate.to_csv(output_dir / "embedding_stability_aggregate.csv", index=False)
summary = {
"expected_folds": arguments.expected_folds,
"distance_definition": "mean foldwise Euclidean distance",
"comparison": "Spearman correlation of laboratory-pair distance vectors",
"invariances": [
"translation",
"rotation",
"reflection",
"positive global scaling",
],
"strategies": list(STRATEGIES),
"seeds": list(SEEDS),
"models": list(MODEL_SPECS),
}
(output_dir / "embedding_stability_summary.json").write_text(
json.dumps(summary, indent=2) + "\n",
encoding="utf-8",
)
print(f"Wrote embedding-stability analysis to: {output_dir}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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