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from dataclasses import dataclass
from typing import Any, Dict, Iterable, List, Mapping, Sequence, Tuple
import numpy as np
import pandas as pd
@dataclass(frozen=True)
class StoppingControls:
target_rank_percentile: float = 1.0
peak_patience: int = 32
min_improvement_delta: float = 0.02
@dataclass(frozen=True)
class ExplorationControls:
stagnation_patience: int = 24
diversity_boost_strength: float = 0.4
dominant_window: int = 128
@dataclass(frozen=True)
class UncertaintyControls:
uncertainty_low_threshold: float = 0.35
uncertainty_patience: int = 32
expected_gain_threshold: float = 0.03
def enforce_control_limits(cfg: Mapping[str, Any]) -> None:
"""Enforce compact control surface to prevent parameter explosion."""
groups = {
"stopping": set((cfg.get("stopping") or {}).keys()),
"exploration": set((cfg.get("exploration") or {}).keys()),
"uncertainty": set((cfg.get("uncertainty") or {}).keys()),
}
for name, keys in groups.items():
if len(keys) > 3:
raise ValueError(f"{name} parameter count exceeds limit (3): {sorted(keys)}")
def controls_from_config(cfg: Mapping[str, Any]) -> tuple[StoppingControls, ExplorationControls, UncertaintyControls]:
stop_cfg = cfg.get("stopping") or {}
exp_cfg = cfg.get("exploration") or {}
unc_cfg = cfg.get("uncertainty") or {}
return (
StoppingControls(
target_rank_percentile=float(stop_cfg.get("target_rank_percentile", 1.0)),
peak_patience=int(stop_cfg.get("peak_patience", 32)),
min_improvement_delta=float(stop_cfg.get("min_improvement_delta", 0.02)),
),
ExplorationControls(
stagnation_patience=int(exp_cfg.get("stagnation_patience", 24)),
diversity_boost_strength=float(exp_cfg.get("diversity_boost_strength", 0.4)),
dominant_window=int(exp_cfg.get("dominant_window", 128)),
),
UncertaintyControls(
uncertainty_low_threshold=float(unc_cfg.get("uncertainty_low_threshold", 0.35)),
uncertainty_patience=int(unc_cfg.get("uncertainty_patience", 32)),
expected_gain_threshold=float(unc_cfg.get("expected_gain_threshold", 0.03)),
),
)
def _safe_float(value: Any, default: float = np.nan) -> float:
try:
x = float(value)
return x if np.isfinite(x) else float(default)
except Exception:
return float(default)
def _best_rank_percentile(selected_ids: Sequence[str], truth_map: Mapping[str, Mapping[str, float]]) -> float:
values: List[float] = []
for lid in selected_ids:
info = truth_map.get(str(lid))
if info is None:
continue
values.append(float(info.get("rank_percentile", 100.0)))
if not values:
return 100.0
return float(np.min(np.asarray(values, dtype=float)))
def _dominant_cluster_from_recent(selected_rows: Sequence[dict[str, Any]], window: int) -> int | None:
if not selected_rows:
return None
recent = selected_rows[-max(1, int(window)) :]
cnt: Dict[int, int] = {}
for row in recent:
cid = int(_safe_float(row.get("cluster_id", -1), default=-1))
if cid < 0:
continue
cnt[cid] = cnt.get(cid, 0) + 1
if not cnt:
return None
return int(max(cnt.items(), key=lambda kv: kv[1])[0])
def _expected_gain_proxy(remaining: pd.DataFrame, incumbent_best: float) -> float:
pred = pd.to_numeric(remaining.get("predicted_score_prebatch", np.nan), errors="coerce").dropna()
if pred.empty:
return 0.0
optimistic = float(np.quantile(pred.to_numpy(dtype=float), 0.05))
# Lower score is better.
return float(max(0.0, incumbent_best - optimistic))
def simulate_multifidelity_policy(
order_df: pd.DataFrame,
*,
truth_map: Mapping[str, Mapping[str, float]],
budget: int,
min_budget: int,
max_budget: int,
wall_time_per_dock: float,
max_wall_time_seconds: float | None,
stopping: StoppingControls,
exploration: ExplorationControls,
uncertainty: UncertaintyControls,
) -> tuple[pd.DataFrame, pd.DataFrame]:
"""Run compact multifidelity policy simulation on a pre-ranked candidate order.
Default mode exploits dominant cluster. Diversity fallback is enabled only under
dominant-cluster stagnation, then automatically disabled.
"""
if order_df.empty:
return order_df.copy(), pd.DataFrame()
cap = int(max(1, min(int(budget), int(max_budget), int(order_df.shape[0]))))
remaining = order_df.sort_values("step").head(cap).copy().reset_index(drop=True)
selected_rows: List[dict[str, Any]] = []
selected_ids: List[str] = []
trace_rows: List[dict[str, Any]] = []
cluster_selected_counts: Dict[int, int] = {}
dominant_cluster: int | None = None
dominant_best_score = np.inf
dominant_stagnant_rounds = 0
diversity_steps_left = 0
rank_history: List[float] = []
uncertainty_history: List[float] = []
stop_reason = "budget_cap_reached"
for i in range(cap):
if remaining.empty:
stop_reason = "pool_exhausted"
break
if diversity_steps_left > 0:
cluster_counts_now = {
int(_safe_float(c, -1)): int(v) for c, v in cluster_selected_counts.items() if int(c) >= 0
}
min_seen = min(cluster_counts_now.values()) if cluster_counts_now else 0
candidate_clusters = {cid for cid, v in cluster_counts_now.items() if v <= min_seen}
cand = remaining[remaining["cluster_id"].isin(candidate_clusters)].head(1)
if cand.empty:
cand = remaining.head(1)
selection_mode = "diversity_fallback"
diversity_steps_left -= 1
else:
dominant_cluster = _dominant_cluster_from_recent(selected_rows, exploration.dominant_window)
if dominant_cluster is not None:
cand = remaining[remaining["cluster_id"] == dominant_cluster].head(1)
if cand.empty:
cand = remaining.head(1)
selection_mode = "dominant_unavailable"
else:
selection_mode = "dominant_exploit"
else:
cand = remaining.head(1)
selection_mode = "initial_explore"
row = cand.iloc[0].to_dict()
remaining = remaining.drop(index=int(cand.index[0])).reset_index(drop=True)
lid = str(row.get("ligand_id"))
cid = int(_safe_float(row.get("cluster_id", -1), -1))
dscore = _safe_float(row.get("docking_score", np.nan), np.nan)
unc = _safe_float(row.get("predicted_uncertainty_prebatch", np.nan), np.nan)
selected_rows.append(row)
selected_ids.append(lid)
cluster_selected_counts[cid] = int(cluster_selected_counts.get(cid, 0) + 1)
if np.isfinite(unc):
uncertainty_history.append(float(unc))
if dominant_cluster is not None and cid == dominant_cluster:
if np.isfinite(dscore) and (dscore < dominant_best_score - stopping.min_improvement_delta):
dominant_best_score = float(dscore)
dominant_stagnant_rounds = 0
else:
dominant_stagnant_rounds += 1
best_rank_pct = _best_rank_percentile(selected_ids, truth_map)
rank_history.append(best_rank_pct)
patience = max(2, int(stopping.peak_patience))
if len(rank_history) >= patience:
slope = float(rank_history[-patience] - rank_history[-1])
else:
slope = np.nan
incumbent_best = float(np.nanmin(pd.to_numeric(pd.DataFrame(selected_rows)["docking_score"], errors="coerce").to_numpy(dtype=float)))
expected_gain = _expected_gain_proxy(remaining, incumbent_best)
u_pat = max(2, int(uncertainty.uncertainty_patience))
recent_unc = uncertainty_history[-u_pat:] if uncertainty_history else []
mean_unc = float(np.mean(np.asarray(recent_unc, dtype=float))) if recent_unc else np.nan
est_wall = float((i + 1) * wall_time_per_dock)
rank_target_hit = bool(best_rank_pct <= float(stopping.target_rank_percentile))
slope_stagnation = bool(np.isfinite(slope) and abs(float(slope)) <= float(stopping.min_improvement_delta))
uncertainty_low = bool(np.isfinite(mean_unc) and mean_unc <= float(uncertainty.uncertainty_low_threshold))
gain_low = bool(float(expected_gain) <= float(uncertainty.expected_gain_threshold))
dominant_plateau = bool(dominant_stagnant_rounds >= int(exploration.stagnation_patience))
allow_stop = bool((i + 1) >= int(min_budget))
stop_now = False
if allow_stop and rank_target_hit and slope_stagnation and gain_low and uncertainty_low:
stop_now = True
stop_reason = "early_peak_dynamic_stop"
elif max_wall_time_seconds is not None and np.isfinite(float(max_wall_time_seconds)) and est_wall >= float(max_wall_time_seconds):
stop_now = True
stop_reason = "max_wall_time_reached"
# Diversity fallback is only activated on dominant-cluster stagnation.
fallback_triggered = False
if (
not stop_now
and diversity_steps_left <= 0
and dominant_plateau
and (i + 1) >= int(min_budget)
and not rank_target_hit
):
diversity_steps_left = max(1, int(round(8.0 * float(exploration.diversity_boost_strength))))
fallback_triggered = True
trace_rows.append(
{
"step": int(i),
"ligand_id": lid,
"selection_mode": selection_mode,
"stop_reason": stop_reason if stop_now else "",
"best_rank_percentile": float(best_rank_pct),
"rank_improvement_slope": float(slope) if np.isfinite(slope) else np.nan,
"expected_gain_proxy": float(expected_gain),
"mean_uncertainty": float(mean_unc) if np.isfinite(mean_unc) else np.nan,
"rank_target_hit": bool(rank_target_hit),
"slope_stagnation": bool(slope_stagnation),
"uncertainty_low": bool(uncertainty_low),
"dominant_plateau": bool(dominant_plateau),
"fallback_triggered": bool(fallback_triggered),
"diversity_steps_left": int(diversity_steps_left),
"dominant_cluster": int(dominant_cluster) if dominant_cluster is not None else -1,
"dynamic_budget_cap": int(cap),
"estimated_wall_seconds": float(est_wall),
}
)
if stop_now:
break
selected_df = pd.DataFrame(selected_rows).reset_index(drop=True)
if not selected_df.empty:
selected_df = selected_df.copy()
selected_df["step"] = np.arange(selected_df.shape[0], dtype=int)
trace_df = pd.DataFrame(trace_rows)
return selected_df, trace_df
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