File size: 8,683 Bytes
bb6d2aa | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 | """Topology-mass-preserving hydrocarbon plan reference.
This module is hydrocarbon-only. It does not import or modify the lactam
catalog, decoder, property model, SMILES builder, plan space, or loss.
"""
from __future__ import annotations
import math
from dataclasses import dataclass
from typing import Any
from staplebridge.chemistry.state import StapleState
from staplebridge.data.schemas import BuildingBlock
from staplebridge.hydrocarbon.curriculum import (
HydrocarbonStaplePlan,
build_hydrocarbon_demonstration_path,
)
@dataclass
class FactorizedPlanReferenceConfig:
"""Configuration read from ``hydrocarbon.reference``.
Defaults were preregistered without property labels from the component
scale audit: MotifSupportAnchorPrior/geometry is primary and frozen ESM2
delta is only a weak regularizer. CatalogBlockPrior is a legality check and
diagnostic, not a soft ranking term.
"""
enabled: bool = False
geometry_coefficient: float = 1.0
esm2_coefficient: float = 0.1
temperature: float = 1.0
block_legality_only: bool = True
@classmethod
def from_config(
cls, root_cfg: dict[str, Any] | None
) -> "FactorizedPlanReferenceConfig":
root_cfg = dict(root_cfg or {})
hydro = dict(root_cfg.get("hydrocarbon") or {})
section = dict(hydro.get("reference") or {})
within = dict(section.get("within_mode") or {})
return cls(
enabled=bool(section.get("factorized_plan_reference", False)),
geometry_coefficient=float(within.get("geometry_coefficient", 1.0)),
esm2_coefficient=float(within.get("esm2_coefficient", 0.1)),
temperature=max(float(within.get("temperature", 1.0)), 1e-8),
block_legality_only=bool(within.get("block_legality_only", True)),
)
def describe(self) -> dict[str, Any]:
return {
"factorized_plan_reference": bool(self.enabled),
"geometry_coefficient": float(self.geometry_coefficient),
"esm2_coefficient": float(self.esm2_coefficient),
"temperature": float(self.temperature),
"block_legality_only": bool(self.block_legality_only),
"uses_property_labels": False,
"normalization": "softmax separately within each feasible mode",
}
class FactorizedPlanReference:
"""Compute ``q_mode(mode|lead) * q_within(plan|lead,mode)``.
The supplied ``mode_prior`` owns the StaPep probability and empirical
beta. Within-mode components are normalized separately, so they cannot
change the total mass assigned to a topology.
"""
def __init__(
self,
mode_prior: Any,
catalog: list[BuildingBlock],
peptide_prior: Any,
anchor_prior: Any,
block_prior: Any,
config: FactorizedPlanReferenceConfig,
) -> None:
self.mode_prior = mode_prior
self.catalog = list(catalog)
self.catalog_index = {block.block_id: block for block in catalog}
self.peptide_prior = peptide_prior
self.anchor_prior = anchor_prior
self.block_prior = block_prior
self.cfg = config
self.last_diagnostics: list[dict[str, Any]] = []
@staticmethod
def _softmax(values: list[float]) -> list[float]:
if not values:
return []
peak = max(values)
exponentials = [math.exp(value - peak) for value in values]
total = sum(exponentials)
if total <= 0.0 or not math.isfinite(total):
return [1.0 / len(values)] * len(values)
probabilities = [value / total for value in exponentials]
if len(probabilities) > 1:
probabilities[-1] = 1.0 - sum(probabilities[:-1])
return probabilities
def weights(
self,
initial: StapleState,
plans: list[HydrocarbonStaplePlan],
context: dict[str, Any] | None = None,
) -> list[float]:
"""Return normalized factorized probabilities in ``plans`` order."""
if not plans:
self.last_diagnostics = []
return []
context = dict(context or {})
terminals = [
build_hydrocarbon_demonstration_path(initial, plan, self.catalog)[-1]
for plan in plans
]
esm2_scores = self.peptide_prior.batch_score_transitions(
initial, terminals, context
)
feasible_modes: list[tuple[str, int]] = []
for plan in plans:
mode = (plan.ordered_pair, plan.spacing)
if mode not in feasible_modes:
feasible_modes.append(mode)
tilted = [self.mode_prior.tilted_weight(mode) for mode in feasible_modes]
tilted_total = sum(tilted)
if tilted_total <= 0.0:
self.last_diagnostics = []
return [0.0] * len(plans)
q_mode = {
mode: weight / tilted_total for mode, weight in zip(feasible_modes, tilted)
}
if len(feasible_modes) > 1:
q_mode[feasible_modes[-1]] = 1.0 - sum(
q_mode[mode] for mode in feasible_modes[:-1]
)
logits: list[float] = []
diagnostics: list[dict[str, Any]] = []
for plan, terminal, esm2_score in zip(plans, terminals, esm2_scores):
block = self.catalog_index.get(plan.block_id)
if block is None:
anchor_score = float("-inf")
block_score = float("-inf")
anchor_components: dict[str, float] = {}
legal = False
else:
anchor_context = dict(context)
anchor_context["return_components"] = True
anchor_score = float(
self.anchor_prior.score_anchor(
terminal.sequence_tokens, plan.anchor_pair, anchor_context
)
)
anchor_components = dict(anchor_context.get("_components") or {})
block_score = float(
self.block_prior.score_block(
terminal.sequence_tokens, plan.anchor_pair, block, context
)
)
legal = math.isfinite(anchor_score) and math.isfinite(block_score)
logit = (
self.cfg.geometry_coefficient * anchor_score
+ self.cfg.esm2_coefficient * float(esm2_score)
) / self.cfg.temperature
if not legal:
logit = float("-inf")
logits.append(float(logit))
diagnostics.append(
{
"mode": f"{plan.ordered_pair}/i,i+{plan.spacing}",
"anchor_pair": list(plan.anchor_pair),
"block_id": plan.block_id,
"stapep_probability": self.mode_prior.probability(
(plan.ordered_pair, plan.spacing)
),
"stapep_tilted_weight": self.mode_prior.tilted_weight(
(plan.ordered_pair, plan.spacing)
),
"q_mode_target": q_mode[(plan.ordered_pair, plan.spacing)],
"anchor_score_raw": anchor_score,
"anchor_components": anchor_components,
"esm2_delta_raw": float(esm2_score),
"block_score_diagnostic_only": block_score,
"block_legal": bool(legal),
"within_mode_logit": float(logit),
}
)
weights = [0.0] * len(plans)
for mode in feasible_modes:
indices = [
index
for index, plan in enumerate(plans)
if (plan.ordered_pair, plan.spacing) == mode
]
mode_logits = [logits[index] for index in indices]
finite = [math.isfinite(value) for value in mode_logits]
if not any(finite):
continue
masked = [value if ok else -1e30 for value, ok in zip(mode_logits, finite)]
within = self._softmax(masked)
for local_index, plan_index in enumerate(indices):
diagnostics[plan_index]["q_within_mode"] = float(within[local_index])
weights[plan_index] = q_mode[mode] * within[local_index]
if len(indices) > 1:
weights[indices[-1]] = q_mode[mode] - sum(
weights[index] for index in indices[:-1]
)
for index, weight in enumerate(weights):
diagnostics[index]["q_ref"] = float(weight)
self.last_diagnostics = diagnostics
return weights
|