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ccbe063 | 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 | """Subset-construction methods: mosaic and gradient.
Both methods take a per-sequence score (typically `contrast_hvlv`) and
produce N_SUBSETS lists of pool indices of length TARGET_SIZE.
Defaults match the published SF-Cluster Phase XII protocol:
N_SUBSETS = 12
TARGET_SIZE = 32
mosaic seeds: s = 0, 1, ..., N_SUBSETS-1
gradient seeds: bin_i * 10 + s for s in {0, 1, 2}, bin_i in {0..3}
"""
from __future__ import annotations
from pathlib import Path
from typing import List, Optional, Sequence
import numpy as np
from .pool import Pool, pool_msa, write_a3m
from .score import contrast_hvlv
N_SUBSETS = 12
TARGET_SIZE = 32
def _subsample(indices: Sequence[int], size: int, rng: np.random.Generator) -> List[int]:
"""Sample `size` items from `indices` without replacement if possible,
with replacement otherwise. Empty input returns []."""
idx = list(indices)
if len(idx) == 0:
return []
if len(idx) >= size:
return list(rng.choice(idx, size=size, replace=False))
return list(rng.choice(idx, size=size, replace=True))
# ---------------------------------------------------------------------------
# Method: mosaic
# ---------------------------------------------------------------------------
def method_mosaic(score: np.ndarray,
n_subsets: int = N_SUBSETS,
subset_size: int = TARGET_SIZE,
*,
high_n: int = 11,
low_n: int = 11,
mid_n: int = 10) -> List[List[int]]:
"""Tri-stratified mosaic: each subset mixes high/low/mid score tiers.
Pool is tri-stratified on `score` (low / mid / high terciles), and each of
`n_subsets` subsets samples (high_n + low_n + mid_n) = subset_size items.
Seeds: subset s uses np.random.default_rng(seed=s).
Args:
score: (N,) per-pool-sequence score (e.g., contrast_hvlv).
n_subsets: number of subsets to build (default 12).
subset_size: total seqs per subset; must equal high_n+low_n+mid_n.
high_n, low_n, mid_n: per-tier sample counts (defaults 11/11/10).
Returns:
list of n_subsets lists of pool indices, length == subset_size each.
"""
if high_n + low_n + mid_n != subset_size:
raise ValueError(
f"high_n+low_n+mid_n ({high_n+low_n+mid_n}) != subset_size ({subset_size})"
)
score = np.asarray(score)
if score.ndim != 1:
raise ValueError("score must be 1-D")
N = score.shape[0]
if N == 0:
raise ValueError("empty score array")
sorted_idx = np.argsort(score)
low_group = list(sorted_idx[: N // 3])
high_group = list(sorted_idx[2 * N // 3 :])
mid_group = list(sorted_idx[N // 3 : 2 * N // 3])
subsets: List[List[int]] = []
for s in range(n_subsets):
rng = np.random.default_rng(seed=s)
hi = _subsample(high_group, high_n, rng)
lo = _subsample(low_group, low_n, rng)
mid = _subsample(mid_group, mid_n, rng)
subsets.append([int(x) for x in (hi + lo + mid)])
return subsets
# ---------------------------------------------------------------------------
# Method: gradient
# ---------------------------------------------------------------------------
def method_gradient(score: np.ndarray,
n_subsets: int = N_SUBSETS,
subset_size: int = TARGET_SIZE,
*,
n_bins: int = 4,
subsets_per_bin: int = 3) -> List[List[int]]:
"""Homogeneous per-quartile subsets along the `score` gradient.
Pool is split into `n_bins` equal-size bins on sorted score, then for each
bin `subsets_per_bin` subsets are drawn entirely from within that bin.
Default 4 bins × 3 subsets-per-bin = 12 subsets.
Seeds: bin_i in [0..n_bins-1], s in [0..subsets_per_bin-1] use
np.random.default_rng(seed=bin_i*10 + s).
Args:
score: (N,) per-pool-sequence score.
n_subsets: expected total (must == n_bins * subsets_per_bin).
subset_size: seqs per subset.
n_bins: number of score quantile bins (default 4).
subsets_per_bin: subsets drawn per bin (default 3).
Returns:
list of n_subsets lists of pool indices.
"""
if n_bins * subsets_per_bin != n_subsets:
raise ValueError(
f"n_bins*subsets_per_bin ({n_bins*subsets_per_bin}) != n_subsets ({n_subsets})"
)
score = np.asarray(score)
if score.ndim != 1:
raise ValueError("score must be 1-D")
N = score.shape[0]
if N == 0:
raise ValueError("empty score array")
sorted_idx = np.argsort(score)
# Equal-quantile bins by integer split (matches reference impl for n_bins=4).
bins: List[List[int]] = []
for b in range(n_bins):
start = (b * N) // n_bins
end = ((b + 1) * N) // n_bins
bins.append(list(sorted_idx[start:end]))
subsets: List[List[int]] = []
for bin_i, bin_idx in enumerate(bins):
for s in range(subsets_per_bin):
rng = np.random.default_rng(seed=bin_i * 10 + s)
chosen = _subsample(bin_idx, subset_size, rng)
subsets.append([int(x) for x in chosen])
return subsets
# ---------------------------------------------------------------------------
# High-level convenience: build_subsets
# ---------------------------------------------------------------------------
def _write_subset_a3ms(pool: Pool,
subsets: List[List[int]],
out_dir: Path,
method: str,
query_index: int = 0) -> List[Path]:
"""Write one A3M per subset; query (pool[query_index]) is always first."""
out_dir = Path(out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
q_header = pool.headers[query_index]
q_seq = pool.sequences[query_index]
paths: List[Path] = []
for s_i, idx_list in enumerate(subsets):
seen = {q_header}
seqs_for_file = [(q_header, q_seq)]
for i in idx_list:
h = pool.headers[i]
if h in seen:
continue
seen.add(h)
seqs_for_file.append((h, pool.sequences[i]))
fname = out_dir / f"{method}_subset_{s_i:03d}.a3m"
write_a3m(fname, pool.header_line, seqs_for_file)
paths.append(fname)
return paths
def build_subsets(a3m_path: str | Path,
fi_npy_path: str | Path,
method: str = "mosaic",
*,
n_subsets: int = N_SUBSETS,
subset_size: int = TARGET_SIZE,
hv_percentile: float = 80.0,
out_dir: Optional[str | Path] = None,
query_index: int = 0):
"""End-to-end: pool -> score -> subset indices [-> A3M files].
Args:
a3m_path: input filtered A3M.
fi_npy_path: per-residue FI matrix (N_seq, L) .npy.
method: "mosaic" or "gradient".
n_subsets: default 12.
subset_size: default 32.
hv_percentile: HV-column variance percentile for contrast_hvlv.
out_dir: if given, write one A3M per subset there.
query_index: which pool row is the query seq (placed first).
Returns:
(pool, score, subsets) or (pool, score, subsets, paths) if out_dir.
"""
pool = pool_msa(a3m_path, fi_npy_path)
score = contrast_hvlv(pool.fi_matrix, percentile=hv_percentile)
if method == "mosaic":
subsets = method_mosaic(score, n_subsets=n_subsets, subset_size=subset_size)
elif method == "gradient":
subsets = method_gradient(score, n_subsets=n_subsets, subset_size=subset_size)
else:
raise ValueError(f"unknown method: {method!r} (expected 'mosaic' or 'gradient')")
if out_dir is None:
return pool, score, subsets
paths = _write_subset_a3ms(pool, subsets, Path(out_dir), method,
query_index=query_index)
return pool, score, subsets, paths
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