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"""Utility helpers for dataset loading."""
from __future__ import annotations
import logging
from typing import Iterable, Sequence
import pandas as pd
EXPECTED_COLUMNS = ("id", "heavy_seq", "light_seq", "label")
OPTIONAL_COLUMNS = ("source", "is_test")
LOGGER = logging.getLogger("polyreact.data")
_DEFAULT_ALIASES: dict[str, Sequence[str]] = {
"id": ("id", "sequence_id", "antibody_id", "uid"),
"heavy_seq": ("heavy_seq", "heavy", "heavy_chain", "H", "H_chain"),
"light_seq": ("light_seq", "light", "light_chain", "L", "L_chain"),
"label": ("label", "polyreactive", "is_polyreactive", "class", "target"),
}
DEFAULT_LABEL_MAP: dict[str | int | float | bool, int] = {
1: 1,
0: 0,
"1": 1,
"0": 0,
True: 1,
False: 0,
"true": 1,
"false": 0,
"polyreactive": 1,
"non-polyreactive": 0,
"poly": 1,
"non": 0,
"positive": 1,
"negative": 0,
}
def _normalize_label_key(value: object) -> object:
if isinstance(value, str):
trimmed = value.strip().lower()
if trimmed in {
"polyreactive",
"non-polyreactive",
"poly",
"non",
"positive",
"negative",
"high",
"low",
"pos",
"neg",
"1",
"0",
"true",
"false",
}:
return trimmed
if trimmed.isdigit():
return trimmed
return value
def ensure_columns(frame: pd.DataFrame, *, heavy_only: bool = True) -> pd.DataFrame:
"""Validate and coerce dataframe columns to the canonical format."""
frame = frame.copy()
for column in ("id", "heavy_seq", "label"):
if column not in frame.columns:
msg = f"Required column '{column}' missing from dataframe"
raise KeyError(msg)
if "light_seq" not in frame.columns:
frame["light_seq"] = ""
if heavy_only:
frame["light_seq"] = ""
frame["id"] = frame["id"].astype(str)
frame["heavy_seq"] = frame["heavy_seq"].fillna("").astype(str)
frame["light_seq"] = frame["light_seq"].fillna("").astype(str)
frame["label"] = frame["label"].astype(int)
ordered = list(EXPECTED_COLUMNS) + [
col for col in frame.columns if col not in EXPECTED_COLUMNS
]
return frame[ordered]
def standardize_frame(
frame: pd.DataFrame,
*,
source: str,
heavy_only: bool = True,
column_aliases: dict[str, Sequence[str]] | None = None,
label_map: dict[str | int | float | bool, int] | None = None,
is_test: bool | None = None,
) -> pd.DataFrame:
"""Rename columns using aliases and coerce labels to integers."""
aliases = {**_DEFAULT_ALIASES}
if column_aliases:
for key, values in column_aliases.items():
aliases[key] = tuple(values) + tuple(aliases.get(key, ()))
rename_map: dict[str, str] = {}
for target, candidates in aliases.items():
if target in frame.columns:
continue
for candidate in candidates:
if candidate in frame.columns and candidate not in rename_map:
rename_map[candidate] = target
break
normalized = frame.rename(columns=rename_map).copy()
if "light_seq" not in normalized.columns:
normalized["light_seq"] = ""
label_lookup = label_map or DEFAULT_LABEL_MAP
normalized["label"] = normalized["label"].map(lambda x: label_lookup.get(_normalize_label_key(x)))
if normalized["label"].isnull().any():
msg = "Label column contains unmapped or missing values"
raise ValueError(msg)
normalized["source"] = source
if is_test is not None:
normalized["is_test"] = bool(is_test)
normalized = ensure_columns(normalized, heavy_only=heavy_only)
return normalized
def deduplicate_sequences(
frames: Iterable[pd.DataFrame],
*,
heavy_only: bool = True,
key_columns: Sequence[str] | None = None,
keep_intra_frames: set[int] | None = None,
) -> list[pd.DataFrame]:
"""Remove duplicate entries across multiple dataframes with configurable keys."""
if key_columns is None:
key_columns = ["heavy_seq"] if heavy_only else ["heavy_seq", "light_seq"]
keep_intra_frames = keep_intra_frames or set()
seen: set[tuple[str, ...]] = set()
cleaned: list[pd.DataFrame] = []
for frame_idx, frame in enumerate(frames):
valid_columns = [col for col in key_columns if col in frame.columns]
if not valid_columns:
valid_columns = ["heavy_seq"]
mask: list[bool] = []
frame_seen: set[tuple[str, ...]] = set()
allow_intra = frame_idx in keep_intra_frames
for values in frame[valid_columns].itertuples(index=False, name=None):
key = tuple(_normalise_key_value(value) for value in values)
if key in seen:
mask.append(False)
continue
if not allow_intra and key in frame_seen:
mask.append(False)
continue
mask.append(True)
frame_seen.add(key)
seen.update(frame_seen)
filtered = frame.loc[mask].reset_index(drop=True)
removed = len(frame) - len(filtered)
if removed:
dataset = "<unknown>"
if "source" in frame.columns and not frame["source"].empty:
dataset = str(frame["source"].iloc[0])
LOGGER.info("Removed %s duplicate sequences from %s", removed, dataset)
cleaned.append(filtered)
return cleaned
def _normalise_key_value(value: object) -> str:
if value is None or (isinstance(value, float) and pd.isna(value)):
return ""
return str(value).strip()
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