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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 | """Recompute the reviewer-requested paired bootstrap table from frozen predictions.
The identity-grouped task resamples molecular-connectivity groups. The
scaffold-aware task resamples the connected scaffold components used to form
the held-out partition. No model is refitted and no prediction is changed.
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
TASKS = {
"canonical_grouped": ("Identity-grouped", "structure_group"),
"scaffold_aware": ("Scaffold-aware", "scaffold_component_group"),
}
REFERENCES = {
"FPNN": "prediction_fpnn",
"GAT": "prediction_gat",
"GCN": "prediction_gcn",
}
CANDIDATE = "prediction_stack_all_plus_descriptors"
METRICS = ("mae", "rmse", "r2")
def metric(name: str, y_true: np.ndarray, y_pred: np.ndarray) -> float:
if name == "mae":
return float(mean_absolute_error(y_true, y_pred))
if name == "rmse":
return float(np.sqrt(mean_squared_error(y_true, y_pred)))
if name == "r2":
return float(r2_score(y_true, y_pred))
raise ValueError(name)
def paired_bootstrap(
y_true: np.ndarray,
candidate: np.ndarray,
reference: np.ndarray,
groups: np.ndarray,
*,
n_resamples: int,
seed: int,
) -> dict[str, dict[str, float | int]]:
unique_groups = np.unique(groups.astype(str))
positions = {group: np.flatnonzero(groups == group) for group in unique_groups}
rng = np.random.default_rng(seed)
samples = {name: [] for name in METRICS}
for _ in range(n_resamples):
sampled_groups = rng.choice(unique_groups, size=len(unique_groups), replace=True)
sampled_positions = np.concatenate([positions[group] for group in sampled_groups])
for name in METRICS:
delta = metric(name, y_true[sampled_positions], candidate[sampled_positions])
delta -= metric(name, y_true[sampled_positions], reference[sampled_positions])
if np.isfinite(delta):
samples[name].append(delta)
result: dict[str, dict[str, float | int]] = {}
for name in METRICS:
point = metric(name, y_true, candidate) - metric(name, y_true, reference)
low, high = np.quantile(np.asarray(samples[name]), [0.025, 0.975])
result[name] = {
"difference_point": float(point),
"ci_low": float(low),
"ci_high": float(high),
"n_resampling_units": int(len(unique_groups)),
"n_valid_resamples": int(len(samples[name])),
}
return result
def fmt(value: float, low: float, high: float, digits: int) -> str:
return f"{value:.{digits}f} [{low:.{digits}f}, {high:.{digits}f}]"
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("--table", type=Path, required=True)
parser.add_argument("--n-resamples", type=int, default=2000)
args = parser.parse_args()
args.output_dir.mkdir(parents=True, exist_ok=True)
nested: dict[str, dict[str, object]] = {}
rows: list[dict[str, object]] = []
for task_dir, (task_label, group_column) in TASKS.items():
nested[task_dir] = {}
for repeat, seed in enumerate((123456, 123457, 123458), start=1):
path = args.artifacts / task_dir / f"seed_{seed}" / "neural_stack" / "test_predictions.csv"
frame = pd.read_csv(path)
y_true = frame["RT"].to_numpy(dtype=float)
candidate = frame[CANDIDATE].to_numpy(dtype=float)
groups = frame[group_column].astype(str).to_numpy()
repeat_result: dict[str, object] = {
"seed": seed,
"resampling_unit": group_column,
"n_rows": int(len(frame)),
"n_resampling_units": int(pd.Series(groups).nunique()),
"comparisons": {},
}
for reference_label, reference_column in REFERENCES.items():
result = paired_bootstrap(
y_true,
candidate,
frame[reference_column].to_numpy(dtype=float),
groups,
n_resamples=args.n_resamples,
seed=seed,
)
repeat_result["comparisons"][reference_label] = result
row: dict[str, object] = {
"task": task_label,
"resampling_unit": group_column,
"reference": reference_label,
"repeat": repeat,
"seed": seed,
"n_rows": len(frame),
"n_resampling_units": pd.Series(groups).nunique(),
}
for name in METRICS:
for key, value in result[name].items():
row[f"{name}_{key}"] = value
rows.append(row)
nested[task_dir][f"seed_{seed}"] = repeat_result
(args.output_dir / "paired_bootstrap_corrected.json").write_text(
json.dumps(nested, indent=2), encoding="utf-8"
)
frame = pd.DataFrame(rows)
frame.to_csv(args.output_dir / "paired_bootstrap_corrected.csv", index=False)
latex = [
r"\begin{table*}[htbp]",
r"\centering",
r"\small",
r"\caption{Paired cluster-bootstrap differences for the full stack relative to each neural base learner. Identity-grouped repeats resample molecular-connectivity groups; scaffold-aware repeats resample scaffold components. Each interval uses 2,000 resamples. Negative $\Delta$MAE and $\Delta$RMSE and positive $\Delta R^2$ favor the full stack.}",
r"\label{tab:paired-bootstrap}",
r"\begin{tabular}{lllccc}",
r"\toprule",
r"Task & Reference & Repeat & $\Delta$MAE (95\% interval), min & $\Delta$RMSE (95\% interval), min & $\Delta R^2$ (95\% interval) \\",
r"\midrule",
]
for task_label in ("Identity-grouped", "Scaffold-aware"):
task_rows = frame.loc[frame["task"] == task_label]
for reference_label in REFERENCES:
for _, row in task_rows.loc[task_rows["reference"] == reference_label].iterrows():
mae = fmt(row.mae_difference_point, row.mae_ci_low, row.mae_ci_high, 3)
rmse = fmt(row.rmse_difference_point, row.rmse_ci_low, row.rmse_ci_high, 3)
r2 = fmt(row.r2_difference_point, row.r2_ci_low, row.r2_ci_high, 3)
latex.append(
f"{task_label} & {reference_label} & {int(row['repeat'])} & {mae} & {rmse} & {r2} \\\\"
)
latex.extend([r"\bottomrule", r"\end{tabular}", r"\end{table*}", ""])
args.table.write_text("\n".join(latex), encoding="utf-8")
if __name__ == "__main__":
main()
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