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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | """Matched FPNN laboratory-encoding ablation on the frozen outer splits.
The submitted/revised FPNN concatenates a learned 64-dimensional laboratory
embedding with the 2,048-bit Morgan fingerprint. This script retains the same
fingerprint, hidden layers, optimizer, target scaling, early stopping, inner
folds, and outer test partitions, replacing only the learned embedding by a
fixed 23-dimensional one-hot laboratory vector.
"""
from __future__ import annotations
import argparse
import json
import random
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.preprocessing import OneHotEncoder
PROJECT_ROOT = Path(__file__).resolve().parents[2]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from src.data import MolecularFeatureExtractor
from src.trainers import NeuralNetworkTrainer
from src.neural_models import FingerprintNN
TASKS = {
"canonical_grouped": ("Identity-grouped", "structure_group"),
"scaffold_aware": ("Scaffold-aware", "scaffold_component_group"),
}
SEEDS = (123456, 123457, 123458)
def set_seed(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def scores(y_true: np.ndarray, y_pred: np.ndarray) -> dict[str, float]:
return {
"r2": float(r2_score(y_true, y_pred)),
"mae": float(mean_absolute_error(y_true, y_pred)),
"rmse": float(np.sqrt(mean_squared_error(y_true, y_pred))),
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--artifacts", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--device", default="auto")
args = parser.parse_args()
args.output_dir.mkdir(parents=True, exist_ok=True)
first = pd.read_csv(
args.artifacts / "canonical_grouped" / "seed_123456" / "split_assignments.csv"
).sort_values("record_index")
extractor = MolecularFeatureExtractor()
fingerprints = np.stack(
[extractor.get_morgan_fingerprint(smiles) for smiles in first["SMILES"].astype(str)]
).astype(np.float32)
device = torch.device(
"cuda" if args.device == "auto" and torch.cuda.is_available()
else "cpu" if args.device == "auto"
else args.device
)
model_base = {
"hidden_dims": [768, 384, 192, 96],
"dropout": 0.15,
"use_batch_norm": True,
"num_labs": None,
"lab_embed_dim": 64,
"input_dropout": 0.1,
}
training = {
"epochs": 600,
"lr": 0.0015,
"weight_decay": 0.00001,
"patience": 60,
"warmup_epochs": 15,
"batch_size": 128,
"gradient_clip": 1.0,
}
rows: list[dict[str, object]] = []
for task_dir, (task_label, group_column) in TASKS.items():
for repeat, seed in enumerate(SEEDS, start=1):
run_dir = args.output_dir / task_dir / f"seed_{seed}"
run_dir.mkdir(parents=True, exist_ok=True)
split = pd.read_csv(
args.artifacts / task_dir / f"seed_{seed}" / "split_assignments.csv"
).sort_values("record_index")
if not np.array_equal(split["record_index"].to_numpy(), first["record_index"].to_numpy()):
raise RuntimeError("Record order changed across frozen split files.")
development = np.flatnonzero(split["outer_split"].eq("development").to_numpy())
test = np.flatnonzero(split["outer_split"].eq("test").to_numpy())
targets = split["RT"].to_numpy(dtype=np.float32)
encoder = OneHotEncoder(sparse_output=False, handle_unknown="error", dtype=np.float32)
encoder.fit(split.loc[development, ["Lab"]])
one_hot = encoder.transform(split[["Lab"]]).astype(np.float32)
fixed_features = np.column_stack([fingerprints, one_hot]).astype(np.float32)
config = dict(model_base, input_dim=int(fixed_features.shape[1]))
fold_test_predictions: list[np.ndarray] = []
fold_metrics: list[dict[str, object]] = []
started = time.perf_counter()
for fold in range(6):
fold_validation = np.flatnonzero(
split["inner_validation_fold"].eq(fold).to_numpy()
)
fold_train = np.setdiff1d(development, fold_validation, assume_unique=False)
if len(fold_validation) == 0 or not np.isin(fold_validation, development).all():
raise RuntimeError(f"Invalid inner fold {fold} for {task_dir}, seed {seed}.")
set_seed(seed + fold)
model = FingerprintNN(**config)
trainer = NeuralNetworkTrainer(
model=model,
device=device,
lr=training["lr"],
weight_decay=training["weight_decay"],
)
metrics, _ = trainer.train_fold(
train_features=fixed_features[fold_train],
train_targets=targets[fold_train],
val_features=fixed_features[fold_validation],
val_targets=targets[fold_validation],
epochs=training["epochs"],
batch_size=training["batch_size"],
patience=training["patience"],
gradient_clip=training["gradient_clip"],
warmup_epochs=training["warmup_epochs"],
verbose=False,
)
trainer.save(str(run_dir / f"fpnn_one_hot_fold_{fold}.pt"))
fold_test_predictions.append(trainer.predict(fixed_features[test]))
fold_metrics.append({"fold": fold, **metrics})
prediction = np.mean(np.stack(fold_test_predictions), axis=0)
elapsed = time.perf_counter() - started
learned = pd.read_csv(
args.artifacts / task_dir / f"seed_{seed}" / "neural_stack" / "test_predictions.csv"
).sort_values("record_index")
if not np.array_equal(learned["record_index"].to_numpy(), split.iloc[test]["record_index"].to_numpy()):
raise RuntimeError("Test-prediction order does not match frozen split assignment.")
learned_scores = scores(targets[test], learned["prediction_fpnn"].to_numpy(dtype=float))
one_hot_scores = scores(targets[test], prediction)
prediction_table = split.iloc[test][
["record_index", "SMILES", "Lab", "RT", "structure_group", "scaffold_component_group"]
].copy()
prediction_table["prediction_fpnn_learned_embedding"] = learned[
"prediction_fpnn"
].to_numpy(dtype=float)
prediction_table["prediction_fpnn_one_hot"] = prediction
prediction_table.to_csv(run_dir / "test_predictions.csv", index=False)
metadata = {
"task": task_dir,
"task_label": task_label,
"repeat": repeat,
"outer_seed": seed,
"device": str(device),
"comparison": "Same FPNN trunk; learned 64-dimensional lab embedding versus fixed 23-dimensional one-hot lab vector.",
"fingerprint": {"radius": 2, "n_bits": 2048, "use_chirality": True},
"group_column": group_column,
"n_test_rows": int(len(test)),
"n_test_groups": int(split.iloc[test][group_column].nunique()),
"model_config": config,
"training_config": training,
"fold_metrics": fold_metrics,
"training_seconds": elapsed,
"learned_embedding": learned_scores,
"one_hot": one_hot_scores,
}
(run_dir / "metrics.json").write_text(json.dumps(metadata, indent=2), encoding="utf-8")
rows.append(
{
"task": task_label,
"repeat": repeat,
"seed": seed,
"encoding": "Learned embedding",
**learned_scores,
}
)
rows.append(
{
"task": task_label,
"repeat": repeat,
"seed": seed,
"encoding": "One-hot vector",
**one_hot_scores,
}
)
per_run = pd.DataFrame(rows)
per_run.to_csv(args.output_dir / "matched_lab_encoding_by_run.csv", index=False)
aggregate = (
per_run.groupby(["task", "encoding"], sort=False)[["r2", "mae", "rmse"]]
.agg(["mean", "std"])
.reset_index()
)
aggregate.columns = ["_".join(str(value) for value in col if value) for col in aggregate.columns]
aggregate.to_csv(args.output_dir / "matched_lab_encoding_aggregate.csv", index=False)
if __name__ == "__main__":
main()
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