File size: 7,313 Bytes
c289d87 | 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 | from __future__ import annotations
from dataclasses import dataclass
from typing import Dict, Iterable, List, Tuple
from libs.adaptive.scheduler import AdaptiveScheduler
@dataclass(frozen=True)
class AdaptivePolicyVariant:
name: str
stop_mode: str # hard | soft | none
cluster_bootstrap_rounds: int = 0
min_cluster_coverage: float = 0.0
min_hypercluster_coverage: float = 0.0
soft_recovery_batches: int = 0
soft_threshold_relax: float = 0.0
soft_model_weight_cap: float = 1.0
soft_exploration_boost: float = 0.0
pre_floor_model_weight_cap: float = 1.0
max_soft_recoveries: int = 1
epsilon_quality: float = 0.10
acceptable_regret: float = 0.10
min_budget: int = 250
max_budget: int = 10000
regret_patience_rounds: int = 6
regret_uncertainty_multiplier: float = 0.5
regret_min_cluster_coverage: float = 0.20
regret_min_hypercluster_coverage: float = 0.20
@dataclass
class PolicyRuntimeState:
soft_recovery_remaining: int = 0
soft_recoveries_used: int = 0
def default_policy_variants() -> Dict[str, AdaptivePolicyVariant]:
return {
"adaptive_hard_stop": AdaptivePolicyVariant(
name="adaptive_hard_stop",
stop_mode="hard",
),
"adaptive_soft_stop": AdaptivePolicyVariant(
name="adaptive_soft_stop",
stop_mode="soft",
soft_recovery_batches=16,
soft_threshold_relax=0.35,
soft_model_weight_cap=0.45,
soft_exploration_boost=0.25,
pre_floor_model_weight_cap=1.0,
max_soft_recoveries=1,
),
"adaptive_no_stop": AdaptivePolicyVariant(
name="adaptive_no_stop",
stop_mode="none",
),
"adaptive_cluster_bootstrap": AdaptivePolicyVariant(
name="adaptive_cluster_bootstrap",
stop_mode="hard",
cluster_bootstrap_rounds=24,
),
"adaptive_soft_stop_cluster_floor": AdaptivePolicyVariant(
name="adaptive_soft_stop_cluster_floor",
stop_mode="soft",
cluster_bootstrap_rounds=20,
min_cluster_coverage=0.60,
min_hypercluster_coverage=0.55,
soft_recovery_batches=20,
soft_threshold_relax=0.40,
soft_model_weight_cap=0.40,
soft_exploration_boost=0.30,
pre_floor_model_weight_cap=0.35,
max_soft_recoveries=2,
),
}
def regret_stop_variant(
*,
name: str = "adaptive_budgeted_regret_stop",
epsilon_quality: float = 0.10,
acceptable_regret: float = 0.10,
min_budget: int = 250,
max_budget: int = 10000,
regret_patience_rounds: int = 6,
regret_uncertainty_multiplier: float = 0.50,
regret_min_cluster_coverage: float = 0.20,
regret_min_hypercluster_coverage: float = 0.20,
) -> AdaptivePolicyVariant:
return AdaptivePolicyVariant(
name=name,
stop_mode="regret",
epsilon_quality=float(epsilon_quality),
acceptable_regret=float(acceptable_regret),
min_budget=int(min_budget),
max_budget=int(max_budget),
regret_patience_rounds=int(regret_patience_rounds),
regret_uncertainty_multiplier=float(regret_uncertainty_multiplier),
regret_min_cluster_coverage=float(regret_min_cluster_coverage),
regret_min_hypercluster_coverage=float(regret_min_hypercluster_coverage),
)
def coverage_fraction(sampled: Iterable[int], total: int) -> float:
if total <= 0:
return 0.0
return float(len(set(sampled)) / float(total))
def coverage_floor_reached(
variant: AdaptivePolicyVariant,
*,
cluster_coverage: float,
hypercluster_coverage: float,
) -> bool:
return bool(
cluster_coverage >= float(variant.min_cluster_coverage)
and hypercluster_coverage >= float(variant.min_hypercluster_coverage)
)
def select_cluster_bootstrap_batch(
scheduler: AdaptiveScheduler,
*,
sampled_clusters: set[int],
batch_size: int,
) -> List[str]:
active = scheduler.queue_manager.ordered_active()
if not active:
return []
selected: List[str] = []
# First pass: one candidate from unsampled clusters.
for item in active:
if item.cluster_id in sampled_clusters:
continue
if item.ligand_id in selected:
continue
selected.append(item.ligand_id)
if len(selected) >= batch_size:
break
# Fill remaining by priority order.
if len(selected) < batch_size:
for item in active:
if item.ligand_id in selected:
continue
selected.append(item.ligand_id)
if len(selected) >= batch_size:
break
for ligand_id in selected:
scheduler.queue_manager.items[ligand_id].times_selected += 1
return selected
def effective_controls(
variant: AdaptivePolicyVariant,
state: PolicyRuntimeState,
*,
coverage_floor_active: bool,
) -> Tuple[float, float, float]:
# Returns (model_weight_cap, exploration_boost, threshold_relax).
model_cap = 1.0
exploration_boost = 0.0
threshold_relax = 0.0
if coverage_floor_active:
model_cap = min(model_cap, float(variant.pre_floor_model_weight_cap))
if state.soft_recovery_remaining > 0:
model_cap = min(model_cap, float(variant.soft_model_weight_cap))
exploration_boost = max(exploration_boost, float(variant.soft_exploration_boost))
threshold_relax = max(threshold_relax, float(variant.soft_threshold_relax))
return float(model_cap), float(exploration_boost), float(threshold_relax)
def apply_stop_policy(
variant: AdaptivePolicyVariant,
state: PolicyRuntimeState,
*,
stop_signal: bool,
stop_reason: str,
coverage_floor_active: bool,
) -> Tuple[bool, str, PolicyRuntimeState]:
mode = str(variant.stop_mode).strip().lower()
# Coverage floor blocks stop decisions in floor-enforced variants.
if coverage_floor_active and stop_signal:
return False, "coverage_floor_blocked_stop", state
if mode == "none":
return False, "no_stop_policy", state
if mode == "hard":
return bool(stop_signal), (stop_reason if stop_signal else "continue"), state
if mode == "regret":
return bool(stop_signal), (stop_reason if stop_signal else "continue"), state
# soft mode
if state.soft_recovery_remaining > 0:
state.soft_recovery_remaining -= 1
if state.soft_recovery_remaining <= 0 and stop_signal:
if state.soft_recoveries_used >= int(variant.max_soft_recoveries):
return True, "soft_recovery_exhausted", state
state.soft_recovery_remaining = int(variant.soft_recovery_batches)
state.soft_recoveries_used += 1
return False, "soft_recovery_restarted", state
return False, "soft_recovery_active", state
if not stop_signal:
return False, "continue", state
if state.soft_recoveries_used >= int(variant.max_soft_recoveries):
return True, "soft_recovery_limit_reached", state
state.soft_recovery_remaining = int(variant.soft_recovery_batches)
state.soft_recoveries_used += 1
return False, "soft_recovery_started", state
|