File size: 5,435 Bytes
25cbcc2 | 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 | from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import numpy as np
import pandas as pd
from PIL import Image
REQUIRED_COLUMNS = (
"sample_id",
"edit_type",
"before",
"after",
"instruction_pos",
"instruction_neg_list",
"instruction_neg_types",
)
PATH_COLUMNS = ("before", "after")
def load_dataset(dataset_root: str | Path) -> pd.DataFrame:
dataset_root = Path(dataset_root)
parquet_path = dataset_root / "benchmark.parquet"
if not parquet_path.exists():
raise FileNotFoundError(f"Missing dataset parquet: {parquet_path}")
return pd.read_parquet(parquet_path)
def as_list(value: Any) -> list[Any]:
if value is None:
return []
if isinstance(value, float) and np.isnan(value):
return []
if isinstance(value, np.ndarray):
return value.tolist()
if isinstance(value, (list, tuple)):
return list(value)
return [value]
def parse_metadata(value: Any) -> dict[str, Any]:
if isinstance(value, dict):
return value
if isinstance(value, str):
try:
parsed = json.loads(value)
return parsed if isinstance(parsed, dict) else {}
except json.JSONDecodeError:
return {}
return {}
def expand_triplets(df: pd.DataFrame) -> pd.DataFrame:
"""Expand edit-level rows into positive/negative verification triplets."""
rows: list[dict[str, Any]] = []
for row_index, row in df.reset_index(drop=True).iterrows():
sample_id = row.get("sample_id", f"row-{row_index:05d}")
base = {
"sample_id": sample_id,
"parquet_row_index": int(row.get("parquet_row_index", row_index)),
"edit_type": row["edit_type"],
"before": row["before"],
"after": row["after"],
}
rows.append(
{
**base,
"instruction": row["instruction_pos"],
"label": 1,
"ground_truth": True,
"example_type": "positive",
"negative_type": "positive",
"negative_index": -1,
}
)
negs = as_list(row["instruction_neg_list"])
neg_types = as_list(row["instruction_neg_types"])
if len(negs) != len(neg_types):
raise ValueError(f"Negative instruction/type length mismatch for {sample_id}")
for neg_index, (instruction, negative_type) in enumerate(zip(negs, neg_types)):
rows.append(
{
**base,
"instruction": instruction,
"label": 0,
"ground_truth": False,
"example_type": "negative",
"negative_type": str(negative_type),
"negative_index": neg_index,
}
)
return pd.DataFrame(rows)
def _is_portable_relative_path(value: Any) -> bool:
if value is None:
return False
path = Path(str(value))
return not path.is_absolute() and ".." not in path.parts
def validate_dataset(dataset_root: str | Path, strict_core: bool = True) -> dict[str, Any]:
"""Validate the public Hugging Face dataset layout."""
dataset_root = Path(dataset_root)
df = load_dataset(dataset_root)
report: dict[str, Any] = {
"row_count": int(len(df)),
"required_missing": [col for col in REQUIRED_COLUMNS if col not in df.columns],
"edit_type_counts": df["edit_type"].value_counts().sort_index().to_dict()
if "edit_type" in df.columns
else {},
}
before_exists = df["before"].map(lambda p: (dataset_root / str(p)).exists())
after_exists = df["after"].map(lambda p: (dataset_root / str(p)).exists())
report["before_paths_resolvable"] = int(before_exists.sum())
report["after_paths_resolvable"] = int(after_exists.sum())
length_matches = []
for _, row in df.iterrows():
length_matches.append(
len(as_list(row["instruction_neg_list"])) == len(as_list(row["instruction_neg_types"]))
)
report["negative_instruction_lengths_match"] = int(sum(length_matches))
non_portable_paths: dict[str, int] = {}
for col in PATH_COLUMNS:
if col in df.columns:
bad_count = int((~df[col].map(_is_portable_relative_path)).sum())
if bad_count:
non_portable_paths[col] = bad_count
report["non_portable_path_columns"] = non_portable_paths
dimensions: dict[str, int] = {}
for path_text in pd.concat([df["before"], df["after"]]).head(100):
path = dataset_root / str(path_text)
if path.exists():
with Image.open(path) as image:
key = f"{image.width}x{image.height}"
dimensions[key] = dimensions.get(key, 0) + 1
report["sampled_image_dimension_counts"] = dimensions
checks = [
not report["required_missing"],
report["before_paths_resolvable"] == len(df),
report["after_paths_resolvable"] == len(df),
report["negative_instruction_lengths_match"] == len(df),
not report["non_portable_path_columns"],
]
if strict_core:
checks.extend(
[
len(df) == 1500,
set(report["edit_type_counts"].values()) == {150},
]
)
report["passed"] = bool(all(checks))
return report
|