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video-language-model
egocentric-video
laboratory
wet-lab
procedural-monitoring
error-detection
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f91d9a0 | 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 | """Pipette mistake / error detection task."""
from __future__ import annotations
from collections import Counter
from pathlib import Path
from typing import Any
from ..io import BENCHMARK_ROOT, read_parquet, select_shard
from ..metrics import bootstrap_sem, classification_scores
from ..parsing import parse_choice_option
from .base import BenchmarkTask, TaskSpec
PMD_OPTIONS = {
"CORRECT",
"ERROR_REUSE",
"ERROR_SURFACE",
"ERROR_RELEASE",
"ERROR_INSTALL",
"ERROR_OTHER",
}
PMD_MISTAKE_TO_OPTION = {
"none": "CORRECT",
"reuse_tip": "ERROR_REUSE",
"reuse_same_media": "ERROR_REUSE",
"reuse_same_tip": "ERROR_REUSE",
"surface_contamination": "ERROR_SURFACE",
"early_release": "ERROR_RELEASE",
"wrong_tip_for_pipette": "ERROR_INSTALL",
}
PMD_NUMBER_TO_OPTION = {
"1": "CORRECT",
"2": "ERROR_REUSE",
"3": "ERROR_SURFACE",
"4": "ERROR_RELEASE",
"5": "ERROR_INSTALL",
"6": "ERROR_OTHER",
}
def pmd_target_option(label: str, mistake_type: str) -> str:
return "CORRECT" if label == "correct" else PMD_MISTAKE_TO_OPTION.get(mistake_type, "ERROR_OTHER")
class PmdTask(BenchmarkTask):
spec = TaskSpec(
name="pmd",
display_name="Error Detection",
default_manifest=BENCHMARK_ROOT / "pmd" / "pmd.parquet",
sort_key="video_id",
)
primary_metric = "binary_balanced_accuracy"
def load_examples(
self,
*,
benchmark_root: Path = BENCHMARK_ROOT,
manifest_path: Path | None = None,
video_root: Path | None = None,
num_shards: int = 1,
shard_index: int = 0,
limit: int | None = None,
) -> list[dict[str, Any]]:
rows = read_parquet(manifest_path or self.default_manifest)
selected: list[dict[str, Any]] = []
root = video_root or benchmark_root
for row in rows:
rel_video = str(row.get("video_path") or "")
if not rel_video:
continue
video_path = root / rel_video
target = pmd_target_option(str(row.get("label")), str(row.get("mistake_type")))
selected.append(
{
**row,
"task": self.name,
"_video_abs": str(video_path),
"target_option": target,
"target_binary": "CORRECT" if target == "CORRECT" else "ERROR",
}
)
selected = sorted(selected, key=lambda row: str(row.get("video_id") or ""))
if limit is not None:
selected = selected[:limit]
return select_shard(selected, num_shards, shard_index)
def parse_record(self, row: dict[str, Any]) -> dict[str, Any]:
if row.get("error"):
return row
pred = parse_choice_option(str(row.get("raw_response") or ""), PMD_OPTIONS, number_map=PMD_NUMBER_TO_OPTION)
pred_binary = None if pred is None else ("CORRECT" if pred == "CORRECT" else "ERROR")
target = str(row.get("target_option") or pmd_target_option(str(row.get("label")), str(row.get("mistake_type"))))
target_binary = "CORRECT" if target == "CORRECT" else "ERROR"
row.update(
{
"target_option": target,
"target_binary": target_binary,
"pred_option": pred,
"pred_binary": pred_binary,
"pred_parse_ok": pred is not None,
"binary_correct": pred_binary == target_binary if pred_binary is not None else False,
"option_correct": pred == target if pred is not None else False,
}
)
return row
def _summary(self, rows: list[dict[str, Any]]) -> dict[str, Any]:
parsed = self.parse_rows(rows)
scored = [row for row in parsed if not row.get("error")]
binary_pairs: list[tuple[str, str | None]] = []
option_pairs: list[tuple[str, str | None]] = []
error_type_pairs: list[tuple[str, str | None]] = []
for row in scored:
target = str(row.get("target_option"))
pred = row.get("pred_option")
target_binary = "CORRECT" if target == "CORRECT" else "ERROR"
pred_binary = None if pred is None else ("CORRECT" if pred == "CORRECT" else "ERROR")
binary_pairs.append((target_binary, pred_binary))
option_pairs.append((target, None if pred is None else str(pred)))
if target != "CORRECT":
error_type_pairs.append((target, None if pred in (None, "CORRECT") else str(pred)))
binary = classification_scores(binary_pairs, labels=["CORRECT", "ERROR"])
option = classification_scores(option_pairs, labels=sorted(PMD_OPTIONS))
error_type = classification_scores(
error_type_pairs,
labels=sorted(option for option in PMD_OPTIONS if option != "CORRECT"),
)
parse_errors = sum(1 for row in scored if row.get("pred_option") is None)
return {
"task": self.name,
"display_name": self.display_name,
"rows": len(rows),
"scored": len(scored),
"errors": sum(1 for row in rows if row.get("error")),
"parse_errors": parse_errors,
"parse_success_rate": (len(scored) - parse_errors) / len(scored) if scored else None,
"binary_accuracy": binary["accuracy"],
"binary_balanced_accuracy": binary["balanced_accuracy"],
"binary_macro_f1": binary["macro_f1"],
"binary_macro_precision": binary["macro_precision"],
"binary_macro_recall": binary["macro_recall"],
"binary_precision": binary["precision"],
"binary_recall": binary["recall"],
"binary_f1": binary["f1"],
"binary_confusion": binary["confusion"],
"option_accuracy": option["accuracy"],
"option_balanced_accuracy": option["balanced_accuracy"],
"option_macro_f1": option["macro_f1"],
"option_confusion": option["confusion"],
"error_type_accuracy": error_type["accuracy"],
"error_type_balanced_accuracy": error_type["balanced_accuracy"],
"error_type_macro_f1": error_type["macro_f1"],
"error_type_macro_precision": error_type["macro_precision"],
"error_type_macro_recall": error_type["macro_recall"],
"error_type_precision": error_type["precision"],
"error_type_recall": error_type["recall"],
"error_type_f1": error_type["f1"],
"error_type_confusion": error_type["confusion"],
"target_counts": dict(Counter(str(row.get("target_option")) for row in scored)),
"pred_counts": dict(Counter(str(row.get("pred_option")) for row in scored)),
}
def score(self, rows: list[dict[str, Any]]) -> dict[str, Any]:
parsed = self.parse_rows(rows)
summary = self._summary(parsed)
for metric in (
"binary_accuracy",
"binary_balanced_accuracy",
"binary_macro_f1",
"binary_macro_precision",
"binary_macro_recall",
"error_type_accuracy",
"error_type_balanced_accuracy",
"error_type_macro_f1",
"error_type_macro_precision",
"error_type_macro_recall",
):
summary[f"{metric}_sem"] = bootstrap_sem(parsed, metric, self._summary, iterations=200)
return summary
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