Datasets:

zaaabik
/

paper_extraction

"""Punctuation-graph-distance features.
For each sample, build the per-layer co-attention token graph
    G_l(t1, t2) = mean over heads of attention from t1 to t2
and treat its row-stochastic dual as a directed weighted graph.  We compute
the shortest-path "attention distance" from every token t to the nearest
punctuation token, where edge weight is
    w(t1, t2) = -log( max(G_l(t1, t2), eps) )
This is the standard negative-log transformation that turns multiplicative
attention chains into additive distances --- the same trick used for
attention flow / attention rollout, applied here to discover discourse
boundaries via punctuation as anchors.
For each layer l, we extract per-sample statistics of {dist(t, punct) : t}:
mean, std, min, max, fraction with finite distance, median.  We also
extract the same statistics for distance to special tokens (CLS = position 0;
SEP = last non-padded position) and the cross-layer std of the
mean-distance-to-punct trajectory.
Pure attention/tokenizer-derived; no class softmax used.
"""
from __future__ import annotations

from pathlib import Path
from typing import List, Tuple

import numpy as np
import torch
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import shortest_path
from tqdm import tqdm


PUNCT_CHARS = set(".,;:!?\"'`()[]{}-—–…")


def _punct_token_ids(tokenizer) -> set:
    out = set()
    vocab = tokenizer.get_vocab()
    for tok, idx in vocab.items():
        s = tok.replace("##", "").replace("Ġ", "").replace("▁", "")
        if s and all(c in PUNCT_CHARS for c in s):
            out.add(idx)
    return out


def _shortest_path_to_set(D: np.ndarray, anchors: np.ndarray) -> np.ndarray:
    """Dijkstra: for each row return min over anchors of shortest-path distance.
    D is a (T, T) directed weighted adjacency where row i has the out-edge
    weights from node i.  anchors is a boolean (T,) mask.
    Returns a (T,) array of distances; np.inf for unreachable.
    """
    sp = shortest_path(csr_matrix(D), directed=True, return_predecessors=False)
    # sp[i, j] = shortest dist i -> j
    if anchors.sum() == 0:
        return np.full(D.shape[0], np.inf, dtype=np.float32)
    return sp[:, anchors].min(axis=1).astype(np.float32)


def _stats(v: np.ndarray) -> np.ndarray:
    finite = v[np.isfinite(v)]
    if len(finite) == 0:
        return np.zeros(6, dtype=np.float32)
    return np.array([
        float(finite.mean()), float(finite.std()),
        float(finite.min()),  float(finite.max()),
        float(np.median(finite)),
        float(len(finite) / max(len(v), 1)),
    ], dtype=np.float32)


def _process_one(args):
    attn_path, punct_mask, T_sample = args
    with np.load(attn_path, allow_pickle=True) as d:
        A = np.asarray(d["attn"]).astype(np.float32)
        T_a = int(d.get("seq_len", A.shape[-1]))
    A = A[:, :, :T_a, :T_a]
    L, _, _, _ = A.shape
    T = min(T_a, T_sample)
    pmask = punct_mask[:T].astype(bool)
    if pmask.sum() == 0:
        # No punctuation: return zeros (matches normal output dim = L*6*2 + 4)
        return np.zeros(L * 6 * 2 + 4, dtype=np.float32)
    # CLS = 0, SEP = T - 1 (RoBERTa/ELECTRA convention)
    cls_mask = np.zeros(T, dtype=bool); cls_mask[0] = True
    sep_mask = np.zeros(T, dtype=bool); sep_mask[T - 1] = True
    feats: List[float] = []
    dist_means_per_layer = []
    for l in range(L):
        G = A[l].mean(axis=0)[:T, :T]
        eps = 1e-6
        # Edge weight = -log(G); zero G → inf weight (no edge)
        W = -np.log(np.clip(G, eps, 1.0))
        # Zero-out self-loops by setting them to a large finite value (not 0 which means no edge)
        np.fill_diagonal(W, 0.0)
        # Compute shortest-path distance from every token to nearest punctuation
        try:
            d_punct = _shortest_path_to_set(W, pmask)
            d_cls = _shortest_path_to_set(W, cls_mask)
        except Exception:
            feats.extend([0.0]*12); dist_means_per_layer.append(0.0); continue
        feats.extend(_stats(d_punct).tolist())
        feats.extend(_stats(d_cls).tolist())
        # mean dist to punct, used for cross-layer std
        finite = d_punct[np.isfinite(d_punct)]
        dist_means_per_layer.append(float(finite.mean()) if len(finite) else 0.0)
    # Cross-layer std of mean dist-to-punct
    dl = np.asarray(dist_means_per_layer, dtype=np.float32)
    cross_std = float(dl.std()) if len(dl) > 0 else 0.0
    cross_mean = float(dl.mean()) if len(dl) > 0 else 0.0
    cross_min = float(dl.min()) if len(dl) > 0 else 0.0
    cross_max = float(dl.max()) if len(dl) > 0 else 0.0
    feats.append(cross_std)
    feats.append(cross_mean)
    feats.append(cross_min)
    feats.append(cross_max)
    return np.array(feats, dtype=np.float32)


def compute_punct_dist(
    attn_dir: Path, tokenizer, model_max_length: int,
    df, text_col: str, n_workers: int = 1,
) -> Tuple[np.ndarray, List[str]]:
    """Return per-sample feature matrix (N, 12*L + 4)."""
    punct_ids = _punct_token_ids(tokenizer)
    rows = []
    iterable = df.reset_index(drop=True)
    # Layer count from the attention dumps, not assumed to be 12.
    with np.load(Path(attn_dir) / f"{int(iterable.iloc[0]['idx']):06d}.npz") as _z:
        L = int(_z["attn"].shape[0])
    # Pre-tokenize to get per-sample punctuation masks
    args_list = []
    for i, r in tqdm(iterable.iterrows(), total=len(iterable), desc="punct-dist tokenize"):
        idx = int(r["idx"])
        enc = tokenizer(r[text_col], truncation=True, max_length=model_max_length,
                         padding="max_length", return_tensors="pt")
        ids = enc["input_ids"][0].tolist()
        is_punct = np.array([tid in punct_ids for tid in ids], dtype=bool)
        T_sample = len(ids)
        args_list.append((attn_dir / f"{idx:06d}.npz", is_punct, T_sample))
    if n_workers <= 1:
        rows = [_process_one(a) for a in tqdm(args_list, desc="punct-dist")]
    else:
        from multiprocessing import Pool
        with Pool(processes=n_workers) as pool:
            rows = list(tqdm(pool.imap(_process_one, args_list, chunksize=8),
                              total=len(args_list), desc=f"punct-dist[{n_workers}w]"))
    arr = np.stack(rows, axis=0)
    cols = []
    for l in range(L):
        for st in ("mean", "std", "min", "max", "median", "finite_frac"):
            cols.append(f"punctD_d2p_{st}_L{l}")
        for st in ("mean", "std", "min", "max", "median", "finite_frac"):
            cols.append(f"punctD_d2c_{st}_L{l}")
    cols.extend(["punctD_crossL_std", "punctD_crossL_mean",
                 "punctD_crossL_min", "punctD_crossL_max"])
    return arr, cols

	"""Punctuation-graph-distance features.

	For each sample, build the per-layer co-attention token graph

	G_l(t1, t2) = mean over heads of attention from t1 to t2

	and treat its row-stochastic dual as a directed weighted graph. We compute
	the shortest-path "attention distance" from every token t to the nearest
	punctuation token, where edge weight is

	w(t1, t2) = -log( max(G_l(t1, t2), eps) )

	This is the standard negative-log transformation that turns multiplicative
	attention chains into additive distances --- the same trick used for
	attention flow / attention rollout, applied here to discover discourse
	boundaries via punctuation as anchors.

	For each layer l, we extract per-sample statistics of {dist(t, punct) : t}:
	mean, std, min, max, fraction with finite distance, median. We also
	extract the same statistics for distance to special tokens (CLS = position 0;
	SEP = last non-padded position) and the cross-layer std of the
	mean-distance-to-punct trajectory.

	Pure attention/tokenizer-derived; no class softmax used.
	"""
	from __future__ import annotations

	from pathlib import Path
	from typing import List, Tuple

	import numpy as np
	import torch
	from scipy.sparse import csr_matrix
	from scipy.sparse.csgraph import shortest_path
	from tqdm import tqdm


	PUNCT_CHARS = set(".,;:!?\"'`()[]{}-—–…")


	def _punct_token_ids(tokenizer) -> set:
	out = set()
	vocab = tokenizer.get_vocab()
	for tok, idx in vocab.items():
	s = tok.replace("##", "").replace("Ġ", "").replace("▁", "")
	if s and all(c in PUNCT_CHARS for c in s):
	out.add(idx)
	return out


	def _shortest_path_to_set(D: np.ndarray, anchors: np.ndarray) -> np.ndarray:
	"""Dijkstra: for each row return min over anchors of shortest-path distance.

	D is a (T, T) directed weighted adjacency where row i has the out-edge
	weights from node i. anchors is a boolean (T,) mask.
	Returns a (T,) array of distances; np.inf for unreachable.
	"""
	sp = shortest_path(csr_matrix(D), directed=True, return_predecessors=False)
	# sp[i, j] = shortest dist i -> j
	if anchors.sum() == 0:
	return np.full(D.shape[0], np.inf, dtype=np.float32)
	return sp[:, anchors].min(axis=1).astype(np.float32)


	def _stats(v: np.ndarray) -> np.ndarray:
	finite = v[np.isfinite(v)]
	if len(finite) == 0:
	return np.zeros(6, dtype=np.float32)
	return np.array([
	float(finite.mean()), float(finite.std()),
	float(finite.min()), float(finite.max()),
	float(np.median(finite)),
	float(len(finite) / max(len(v), 1)),
	], dtype=np.float32)


	def _process_one(args):
	attn_path, punct_mask, T_sample = args
	with np.load(attn_path, allow_pickle=True) as d:
	A = np.asarray(d["attn"]).astype(np.float32)
	T_a = int(d.get("seq_len", A.shape[-1]))
	A = A[:, :, :T_a, :T_a]
	L, _, _, _ = A.shape
	T = min(T_a, T_sample)
	pmask = punct_mask[:T].astype(bool)
	if pmask.sum() == 0:
	# No punctuation: return zeros (matches normal output dim = L62 + 4)
	return np.zeros(L * 6 * 2 + 4, dtype=np.float32)
	# CLS = 0, SEP = T - 1 (RoBERTa/ELECTRA convention)
	cls_mask = np.zeros(T, dtype=bool); cls_mask[0] = True
	sep_mask = np.zeros(T, dtype=bool); sep_mask[T - 1] = True
	feats: List[float] = []
	dist_means_per_layer = []
	for l in range(L):
	G = A[l].mean(axis=0)[:T, :T]
	eps = 1e-6
	# Edge weight = -log(G); zero G → inf weight (no edge)
	W = -np.log(np.clip(G, eps, 1.0))
	# Zero-out self-loops by setting them to a large finite value (not 0 which means no edge)
	np.fill_diagonal(W, 0.0)
	# Compute shortest-path distance from every token to nearest punctuation
	try:
	d_punct = _shortest_path_to_set(W, pmask)
	d_cls = _shortest_path_to_set(W, cls_mask)
	except Exception:
	feats.extend([0.0]*12); dist_means_per_layer.append(0.0); continue
	feats.extend(_stats(d_punct).tolist())
	feats.extend(_stats(d_cls).tolist())
	# mean dist to punct, used for cross-layer std
	finite = d_punct[np.isfinite(d_punct)]
	dist_means_per_layer.append(float(finite.mean()) if len(finite) else 0.0)
	# Cross-layer std of mean dist-to-punct
	dl = np.asarray(dist_means_per_layer, dtype=np.float32)
	cross_std = float(dl.std()) if len(dl) > 0 else 0.0
	cross_mean = float(dl.mean()) if len(dl) > 0 else 0.0
	cross_min = float(dl.min()) if len(dl) > 0 else 0.0
	cross_max = float(dl.max()) if len(dl) > 0 else 0.0
	feats.append(cross_std)
	feats.append(cross_mean)
	feats.append(cross_min)
	feats.append(cross_max)
	return np.array(feats, dtype=np.float32)


	def compute_punct_dist(
	attn_dir: Path, tokenizer, model_max_length: int,
	df, text_col: str, n_workers: int = 1,
	) -> Tuple[np.ndarray, List[str]]:
	"""Return per-sample feature matrix (N, 12*L + 4)."""
	punct_ids = _punct_token_ids(tokenizer)
	rows = []
	iterable = df.reset_index(drop=True)
	# Layer count from the attention dumps, not assumed to be 12.
	with np.load(Path(attn_dir) / f"{int(iterable.iloc[0]['idx']):06d}.npz") as _z:
	L = int(_z["attn"].shape[0])
	# Pre-tokenize to get per-sample punctuation masks
	args_list = []
	for i, r in tqdm(iterable.iterrows(), total=len(iterable), desc="punct-dist tokenize"):
	idx = int(r["idx"])
	enc = tokenizer(r[text_col], truncation=True, max_length=model_max_length,
	padding="max_length", return_tensors="pt")
	ids = enc["input_ids"][0].tolist()
	is_punct = np.array([tid in punct_ids for tid in ids], dtype=bool)
	T_sample = len(ids)
	args_list.append((attn_dir / f"{idx:06d}.npz", is_punct, T_sample))
	if n_workers <= 1:
	rows = [_process_one(a) for a in tqdm(args_list, desc="punct-dist")]
	else:
	from multiprocessing import Pool
	with Pool(processes=n_workers) as pool:
	rows = list(tqdm(pool.imap(_process_one, args_list, chunksize=8),
	total=len(args_list), desc=f"punct-dist[{n_workers}w]"))
	arr = np.stack(rows, axis=0)
	cols = []
	for l in range(L):
	for st in ("mean", "std", "min", "max", "median", "finite_frac"):
	cols.append(f"punctD_d2p_{st}_L{l}")
	for st in ("mean", "std", "min", "max", "median", "finite_frac"):
	cols.append(f"punctD_d2c_{st}_L{l}")
	cols.extend(["punctD_crossL_std", "punctD_crossL_mean",
	"punctD_crossL_min", "punctD_crossL_max"])
	return arr, cols