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8c5a642 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 | """Leakage-safe split builders for A1 baseline evaluation protocols."""
from __future__ import annotations
from dataclasses import asdict, dataclass
from typing import Any
import pandas as pd
@dataclass(frozen=True)
class CrossRunFold:
"""One leave-one-run-out fold for Protocol A."""
protocol: str
subject: str
fold_id: str
test_run: int
train_runs: str
n_train_runs: int
n_test_runs: int
@dataclass(frozen=True)
class WithinRunBlockedSplit:
"""One blocked temporal split with HRF safety gap for Protocol B."""
protocol: str
subject: str
run: int
split_id: str
n_volumes: int
test_start_tr: int
test_end_tr_exclusive: int
gap_tr: int
train_left_start_tr: int
train_left_end_tr_exclusive: int
train_right_start_tr: int
train_right_end_tr_exclusive: int
n_train_volumes: int
n_test_volumes: int
n_gap_excluded_volumes: int
@dataclass(frozen=True)
class WithinRunSkipped:
"""Skipped run when blocked split constraints cannot be satisfied."""
subject: str
run: int
n_volumes: int
reason: str
def build_cross_run_folds(manifest_df: pd.DataFrame) -> pd.DataFrame:
"""Build Protocol A folds (leave-one-run-out within subject)."""
if manifest_df.empty:
return pd.DataFrame()
required_columns = {"subject", "run"}
missing = required_columns.difference(manifest_df.columns)
if missing:
raise ValueError(f"Manifest missing required columns: {sorted(missing)}")
rows: list[CrossRunFold] = []
for subject, subject_df in manifest_df.groupby("subject"):
runs = sorted({int(run) for run in subject_df["run"].tolist()})
if len(runs) < 2:
continue
for test_run in runs:
train_runs = [run for run in runs if run != test_run]
rows.append(
CrossRunFold(
protocol="A_cross_run",
subject=str(subject),
fold_id=f"{subject}_test_run{test_run}",
test_run=int(test_run),
train_runs=",".join(str(run) for run in train_runs),
n_train_runs=len(train_runs),
n_test_runs=1,
)
)
output_df = pd.DataFrame([asdict(row) for row in rows])
if not output_df.empty:
output_df = output_df.sort_values(["subject", "test_run"]).reset_index(drop=True)
return output_df
def _compute_within_run_blocked_bounds(
n_volumes: int,
test_fraction: float,
gap_tr: int,
min_train_volumes: int,
min_test_volumes: int,
) -> tuple[dict[str, int] | None, str | None]:
if n_volumes <= 0:
return None, "non_positive_volume_count"
if not (0.0 < test_fraction < 1.0):
return None, "invalid_test_fraction"
if gap_tr < 0:
return None, "negative_gap"
n_test = max(min_test_volumes, int(round(n_volumes * test_fraction)))
n_test = min(n_test, n_volumes)
if n_test >= n_volumes:
return None, "test_block_covers_entire_run"
# Center block so train windows exist on both sides when possible.
test_start = max(0, (n_volumes - n_test) // 2)
test_end = min(n_volumes, test_start + n_test)
left_train_start = 0
left_train_end = max(0, test_start - gap_tr)
right_train_start = min(n_volumes, test_end + gap_tr)
right_train_end = n_volumes
n_train = (left_train_end - left_train_start) + (right_train_end - right_train_start)
n_gap = (test_start - left_train_end) + (right_train_start - test_end)
if n_train < min_train_volumes:
return None, "insufficient_train_volumes_after_gap"
if n_test < min_test_volumes:
return None, "insufficient_test_volumes"
if left_train_end < left_train_start or right_train_end < right_train_start:
return None, "invalid_train_segment_bounds"
bounds = {
"test_start_tr": int(test_start),
"test_end_tr_exclusive": int(test_end),
"train_left_start_tr": int(left_train_start),
"train_left_end_tr_exclusive": int(left_train_end),
"train_right_start_tr": int(right_train_start),
"train_right_end_tr_exclusive": int(right_train_end),
"n_train_volumes": int(n_train),
"n_test_volumes": int(n_test),
"n_gap_excluded_volumes": int(n_gap),
}
return bounds, None
def build_within_run_blocked_splits(
manifest_df: pd.DataFrame,
test_fraction: float = 0.2,
gap_tr: int = 8,
min_train_volumes: int = 40,
min_test_volumes: int = 20,
) -> tuple[pd.DataFrame, pd.DataFrame]:
"""Build Protocol B blocked temporal splits for each subject-run."""
if manifest_df.empty:
return pd.DataFrame(), pd.DataFrame()
required_columns = {"subject", "run", "n_volumes"}
missing = required_columns.difference(manifest_df.columns)
if missing:
raise ValueError(f"Manifest missing required columns: {sorted(missing)}")
split_rows: list[WithinRunBlockedSplit] = []
skipped_rows: list[WithinRunSkipped] = []
for row in manifest_df.itertuples(index=False):
subject = str(getattr(row, "subject"))
run = int(getattr(row, "run"))
n_volumes = int(getattr(row, "n_volumes"))
bounds, reason = _compute_within_run_blocked_bounds(
n_volumes=n_volumes,
test_fraction=test_fraction,
gap_tr=gap_tr,
min_train_volumes=min_train_volumes,
min_test_volumes=min_test_volumes,
)
if bounds is None:
skipped_rows.append(
WithinRunSkipped(
subject=subject,
run=run,
n_volumes=n_volumes,
reason=str(reason),
)
)
continue
split_rows.append(
WithinRunBlockedSplit(
protocol="B_within_run_blocked",
subject=subject,
run=run,
split_id=f"{subject}_run{run}_blocked",
n_volumes=n_volumes,
test_start_tr=bounds["test_start_tr"],
test_end_tr_exclusive=bounds["test_end_tr_exclusive"],
gap_tr=int(gap_tr),
train_left_start_tr=bounds["train_left_start_tr"],
train_left_end_tr_exclusive=bounds["train_left_end_tr_exclusive"],
train_right_start_tr=bounds["train_right_start_tr"],
train_right_end_tr_exclusive=bounds["train_right_end_tr_exclusive"],
n_train_volumes=bounds["n_train_volumes"],
n_test_volumes=bounds["n_test_volumes"],
n_gap_excluded_volumes=bounds["n_gap_excluded_volumes"],
)
)
split_df = pd.DataFrame([asdict(row) for row in split_rows])
skipped_df = pd.DataFrame([asdict(row) for row in skipped_rows])
if not split_df.empty:
split_df = split_df.sort_values(["subject", "run"]).reset_index(drop=True)
if not skipped_df.empty:
skipped_df = skipped_df.sort_values(["subject", "run"]).reset_index(drop=True)
return split_df, skipped_df
def summarize_split_counts(
manifest_df: pd.DataFrame,
cross_run_df: pd.DataFrame,
within_run_df: pd.DataFrame,
within_run_skipped_df: pd.DataFrame,
) -> dict[str, Any]:
"""Return compact split summary for reporting JSON outputs."""
return {
"n_manifest_rows": int(len(manifest_df)),
"n_subjects_manifest": int(manifest_df["subject"].nunique()) if not manifest_df.empty else 0,
"n_protocol_a_folds": int(len(cross_run_df)),
"n_protocol_b_splits": int(len(within_run_df)),
"n_protocol_b_skipped": int(len(within_run_skipped_df)),
}
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