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from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List
import numpy as np
import pandas as pd
from .metrics import score_metrics
from .policies import PrioritizationPolicy
from .prioritization import cluster_bonus, compute_priority
from .queue_manager import QueueManager
from .state import QueueItem, SchedulerState
from .surrogate_model import SurrogateConfig, SurrogateModel
from .weight_schedule import WeightScheduleConfig, compute_model_weight
@dataclass
class SchedulerConfig:
batch_size: int = 8
init_coverage_fraction: float = 0.25
conservative_deprioritize: bool = True
state_path: str = "results/state.json"
weight_schedule: WeightScheduleConfig = field(default_factory=WeightScheduleConfig)
class AdaptiveScheduler:
"""Adaptive batch scheduler with queue state and online masked surrogate updates."""
def __init__(
self,
config: SchedulerConfig | None = None,
policy: PrioritizationPolicy | None = None,
surrogate_config: SurrogateConfig | None = None,
) -> None:
self.config = config or SchedulerConfig()
self.policy = policy or PrioritizationPolicy()
self.surrogate = SurrogateModel(surrogate_config)
self.state = SchedulerState()
self.queue_manager = QueueManager.from_items([])
self.last_model_weight: float = 0.0
def initialize(
self,
ligands_df: pd.DataFrame,
cluster_map: Dict[str, int],
hypercluster_for_cluster: Dict[int, int],
) -> None:
items: List[QueueItem] = []
cluster_load: Dict[int, int] = {}
for row in ligands_df.itertuples(index=False):
ligand_id = str(row.ligand_id)
cluster_id = int(cluster_map.get(ligand_id, -1))
hypercluster_id = int(hypercluster_for_cluster.get(cluster_id, -1))
bonus = cluster_bonus(cluster_load, cluster_id)
priority = compute_priority(
predicted_utility=0.0,
uncertainty=1.0,
diversity_gain=bonus,
cluster_bonus=bonus,
policy=self.policy,
)
items.append(
QueueItem(
ligand_id=ligand_id,
status="active",
priority=float(priority),
cluster_id=cluster_id,
hypercluster_id=hypercluster_id,
)
)
cluster_load[cluster_id] = cluster_load.get(cluster_id, 0) + 1
self.state = SchedulerState(queue=items, batch_history=[], round_idx=0)
self.queue_manager = QueueManager.from_items(items)
def select_batch(self) -> List[str]:
active = self.queue_manager.ordered_active()
if not active:
# If conservative deprioritization exhausted active queue, reactivate
# deprioritized candidates to continue spending the docking budget.
deprioritized = sorted(
[item for item in self.queue_manager.items.values() if item.status == "deprioritized"],
key=lambda item: item.priority,
reverse=True,
)
if not deprioritized:
return []
for item in deprioritized:
item.status = "active"
active = deprioritized
coverage_count = max(1, int(self.config.batch_size * self.config.init_coverage_fraction))
by_hypercluster: Dict[int, List[QueueItem]] = {}
for item in active:
by_hypercluster.setdefault(item.hypercluster_id, []).append(item)
selected: List[str] = []
# Broad initial coverage.
for _h_id, items in sorted(by_hypercluster.items(), key=lambda x: len(x[1]), reverse=True):
if len(selected) >= coverage_count:
break
selected.append(items[0].ligand_id)
# Fill remaining slots by priority.
for item in active:
if len(selected) >= self.config.batch_size:
break
if item.ligand_id not in selected:
selected.append(item.ligand_id)
for ligand_id in selected:
self.queue_manager.items[ligand_id].times_selected += 1
return selected
@staticmethod
def _mean_or_default(values: list[float], default: float = 0.0) -> float:
if not values:
return float(default)
return float(np.mean(np.asarray(values, dtype=float)))
def update_from_batch(
self,
evaluated_df: pd.DataFrame,
feature_values_df: pd.DataFrame,
feature_masks_df: pd.DataFrame,
) -> Dict[str, float]:
# Mark completed ligands and store latest score.
score_map = dict(zip(evaluated_df["ligand_id"].astype(str), evaluated_df["docking_score"].astype(float)))
for ligand_id, score in score_map.items():
if ligand_id in self.queue_manager.items:
item = self.queue_manager.items[ligand_id]
item.status = "completed"
item.last_score = float(score)
if feature_values_df.empty or feature_masks_df.empty:
raise ValueError("Feature tables cannot be empty in adaptive update")
if "ligand_id" not in feature_values_df.columns or "ligand_id" not in feature_masks_df.columns:
raise ValueError("Feature tables must include ligand_id column")
ligand_order = feature_values_df["ligand_id"].astype(str).tolist()
features = feature_values_df.drop(columns=["ligand_id"]).to_numpy(dtype=float)
masks = feature_masks_df.drop(columns=["ligand_id"]).to_numpy(dtype=float)
feature_names = feature_values_df.drop(columns=["ligand_id"]).columns.tolist()
mask_names = feature_masks_df.drop(columns=["ligand_id"]).columns.tolist()
target = np.full(len(ligand_order), np.nan, dtype=float)
for i, ligand_id in enumerate(ligand_order):
if ligand_id in self.queue_manager.items:
score = self.queue_manager.items[ligand_id].last_score
if score is not None:
target[i] = float(score)
fit_stats = self.surrogate.fit(
features=features,
masks=masks,
y=target,
feature_names=feature_names,
mask_names=mask_names,
)
n_train = int(fit_stats.get("n_train", 0.0))
instability_ratio = float(fit_stats.get("instability_ratio", 1.0))
model_weight = compute_model_weight(
n_samples=n_train,
instability_ratio=instability_ratio,
config=self.config.weight_schedule,
)
if self.surrogate.model is None:
model_weight = 0.0
self.last_model_weight = float(model_weight)
# Predict for active items.
active_items = self.queue_manager.active_items()
active_ids = [item.ligand_id for item in active_items]
completed_items = [item for item in self.queue_manager.items.values() if item.last_score is not None]
global_completed = [float(item.last_score) for item in completed_items if item.last_score is not None]
global_mean_score = self._mean_or_default(global_completed, default=0.0)
cluster_scores: Dict[int, list[float]] = {}
hyper_scores: Dict[int, list[float]] = {}
for item in completed_items:
assert item.last_score is not None
cluster_scores.setdefault(item.cluster_id, []).append(float(item.last_score))
hyper_scores.setdefault(item.hypercluster_id, []).append(float(item.last_score))
if active_ids:
id_to_idx = {lid: idx for idx, lid in enumerate(ligand_order)}
x_active = np.vstack([features[id_to_idx[lid]] for lid in active_ids])
m_active = np.vstack([masks[id_to_idx[lid]] for lid in active_ids])
bundle = self.surrogate.predict_bundle(x_active, m_active)
cluster_load: Dict[int, int] = {}
for item in active_items:
cluster_load[item.cluster_id] = cluster_load.get(item.cluster_id, 0) + 1
for i, ligand_id in enumerate(active_ids):
item = self.queue_manager.items[ligand_id]
cluster_mean = self._mean_or_default(cluster_scores.get(item.cluster_id, []), default=np.nan)
hyper_mean = self._mean_or_default(hyper_scores.get(item.hypercluster_id, []), default=np.nan)
observed_docking = (
cluster_mean
if np.isfinite(cluster_mean)
else hyper_mean
if np.isfinite(hyper_mean)
else global_mean_score
)
docking_component = -float(observed_docking)
model_component = -float(bundle["expected_score"][i])
blended_utility = (1.0 - model_weight) * docking_component + model_weight * model_component
bonus = cluster_bonus(cluster_load, item.cluster_id)
item.priority = compute_priority(
predicted_utility=float(blended_utility),
uncertainty=float(bundle["uncertainty"][i]),
diversity_gain=bonus,
cluster_bonus=bonus,
policy=self.policy,
)
if self.config.conservative_deprioritize:
priorities = np.asarray([self.queue_manager.items[lid].priority for lid in active_ids], dtype=float)
threshold = float(np.quantile(priorities, self.policy.conservative_deprioritize_quantile))
for lid in active_ids:
if self.queue_manager.items[lid].priority < threshold:
self.queue_manager.items[lid].status = "deprioritized"
self.state.round_idx += 1
sm = score_metrics(evaluated_df["docking_score"].astype(float).tolist())
batch_record: Dict[str, float | str] = {
"round_idx": self.state.round_idx,
"batch_size": int(evaluated_df.shape[0]),
"model_weight": float(model_weight),
"n_train": float(n_train),
"train_mae": float(fit_stats.get("train_mae", np.nan)),
"val_mae": float(fit_stats.get("val_mae", np.nan)),
"instability_ratio": float(instability_ratio),
"surrogate_backend": self.surrogate.backend,
**sm,
}
self.state.batch_history.append(batch_record)
return fit_stats
def save_state(self, output_path: str | Path | None = None) -> Path:
target = Path(output_path or self.config.state_path)
self.state.queue = list(self.queue_manager.items.values())
return self.state.save(target)
|