File size: 9,410 Bytes
e4ccd4f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | """ExperimentDesigner: the main entry point for designing experiments."""
from typing import Callable, Dict, List, Optional, Tuple
import torch
from torch import Tensor
from physics_informed_bo.config import OptimizationConfig, OptimizerBackend
from physics_informed_bo.experiment.parameter_space import ParameterSpace
from physics_informed_bo.models.hybrid_model import HybridSurrogate
from physics_informed_bo.priors.prior_manager import PriorManager
from physics_informed_bo.priors.data_prior import DataPrior
from physics_informed_bo.priors.physics_prior import PhysicsPrior
from physics_informed_bo.optimizers.factory import create_optimizer
from physics_informed_bo.optimizers.base_optimizer import BaseOptimizer
class ExperimentDesigner:
"""High-level API for physics-informed Bayesian experiment design.
This is the main user-facing class. It orchestrates:
1. Parameter space definition
2. Physics and data prior management
3. Surrogate model selection and fitting
4. Acquisition function optimization
5. Experiment suggestion
Example:
designer = ExperimentDesigner(
parameter_space=space,
physics_fn=arrhenius_model,
initial_data=(X_init, y_init),
)
# Get next experiment suggestions
next_experiments = designer.suggest(n=3)
# After running experiments, update with results
designer.update(X_new, y_new)
"""
def __init__(
self,
parameter_space: ParameterSpace,
physics_fn: Optional[Callable[[Tensor], Tensor]] = None,
initial_data: Optional[Tuple[Tensor, Tensor]] = None,
config: Optional[OptimizationConfig] = None,
physics_constraints: Optional[List[Dict]] = None,
):
"""
Args:
parameter_space: The experimental parameter space.
physics_fn: Optional physics model function.
initial_data: Optional tuple of (X, y) initial observations.
config: Optimization configuration. Defaults to sensible settings.
physics_constraints: Optional list of physics constraint dicts.
"""
self.parameter_space = parameter_space
self.config = config or OptimizationConfig()
# Set up physics prior
physics_prior = None
if physics_fn is not None:
physics_prior = PhysicsPrior(physics_fn=physics_fn)
if physics_constraints:
for c in physics_constraints:
physics_prior.add_constraint(**c)
# Set up data prior
data_prior = DataPrior()
if initial_data is not None:
X_init, y_init = initial_data
if y_init.dim() == 1:
y_init = y_init.unsqueeze(-1)
data_prior.X = X_init
data_prior.y = y_init
data_prior.feature_names = parameter_space.parameter_names
# Prior manager
self.prior_manager = PriorManager(
physics_prior=physics_prior,
data_prior=data_prior,
)
# Build surrogate
self._surrogate: Optional[HybridSurrogate] = None
self._optimizer: Optional[BaseOptimizer] = None
self._iteration = 0
# Initialize if we have enough data
self._initialize()
def _initialize(self) -> None:
"""Initialize surrogate model and optimizer."""
try:
mode = self.prior_manager.recommend_surrogate_mode()
except ValueError:
# Not enough data or physics model
return
self._surrogate = self.prior_manager.build_surrogate(
mode=mode,
kernel="matern",
noise_variance=self.config.noise_variance,
device=self.config.device,
)
# Set up optimizer
self._optimizer = create_optimizer(self.config)
self._optimizer.set_surrogate(self._surrogate)
self._optimizer.set_bounds(self.parameter_space.bounds)
if self.prior_manager.physics_prior:
self._optimizer.set_physics_prior(self.prior_manager.physics_prior)
def suggest(self, n: int = 1) -> Tensor:
"""Suggest the next n experiments to run.
If not enough data exists for GP-based suggestion, falls back to:
1. Physics-guided sampling (if physics model available)
2. Latin Hypercube sampling (space-filling design)
Args:
n: Number of experiments to suggest.
Returns:
Tensor of shape (n, d) with suggested parameter values.
"""
self._iteration += 1
# Not enough data for BO: use initial design
if self._surrogate is None or self.prior_manager.data_prior.n_observations < 3:
return self._initial_design(n)
# Re-fit surrogate with latest data
data = self.prior_manager.data_prior
if data.n_observations >= 3:
self._surrogate.fit(data.X, data.y)
self._optimizer.set_surrogate(self._surrogate)
# Suggest via optimizer
candidates = self._optimizer.suggest(
n_candidates=n,
X_observed=data.X,
y_observed=data.y,
)
return candidates
def _initial_design(self, n: int) -> Tensor:
"""Generate initial design points when insufficient data for BO.
Uses physics model to prioritize promising regions if available.
"""
if self.prior_manager.physics_prior is not None:
# Sample candidates and pick those with best physics predictions
n_candidates = max(n * 20, 200)
candidates = self.parameter_space.sample_latin_hypercube(n_candidates)
# Filter by physics constraints
candidates = self.prior_manager.physics_prior.get_feasible_subset(candidates)
if len(candidates) < n:
candidates = self.parameter_space.sample_latin_hypercube(n_candidates)
# Rank by physics model prediction
with torch.no_grad():
physics_scores = self.prior_manager.physics_prior.evaluate(candidates)
# Select top-n diverse points (greedy furthest-point selection)
selected = self._select_diverse_top_k(candidates, physics_scores, n)
return selected
else:
return self.parameter_space.sample_latin_hypercube(n)
def _select_diverse_top_k(
self, X: Tensor, scores: Tensor, k: int, top_fraction: float = 0.3
) -> Tensor:
"""Select k diverse points from the top-scoring candidates."""
# Pre-filter to top fraction
n_top = max(k * 3, int(len(X) * top_fraction))
top_idx = scores.argsort(descending=True)[:n_top]
X_top = X[top_idx]
# Greedy furthest-point selection for diversity
selected_idx = [0]
for _ in range(k - 1):
dists = torch.cdist(X_top, X_top[selected_idx]).min(dim=1).values
next_idx = dists.argmax().item()
selected_idx.append(next_idx)
return X_top[selected_idx]
def update(self, X_new: Tensor, y_new: Tensor) -> None:
"""Update the designer with new experimental observations.
Args:
X_new: New input observations (n, d).
y_new: New output observations (n, 1) or (n,).
"""
if y_new.dim() == 1:
y_new = y_new.unsqueeze(-1)
self.prior_manager.update_with_observations(X_new, y_new)
# Re-initialize if we now have enough data
if self._surrogate is None and self.prior_manager.data_prior.n_observations >= 3:
self._initialize()
def get_best(self, maximize: bool = True) -> Dict:
"""Get the best observation so far."""
X_best, y_best = self.prior_manager.data_prior.get_best(maximize)
params = self.parameter_space.to_dict(X_best.unsqueeze(0))[0]
return {"parameters": params, "objective": float(y_best)}
def predict(self, X: Tensor) -> Tuple[Tensor, Tensor]:
"""Get surrogate model predictions at X."""
if self._surrogate is None:
if self.prior_manager.physics_prior:
pred = self.prior_manager.physics_prior.evaluate(X)
return pred.unsqueeze(-1), torch.ones_like(pred.unsqueeze(-1)) * 0.1
raise RuntimeError("No surrogate model fitted yet.")
return self._surrogate.predict(X)
def model_quality(self) -> Dict:
"""Assess current surrogate model quality."""
if self._surrogate is None:
return {"status": "no_model"}
return self._surrogate.physics_model_quality()
def summary(self) -> Dict:
"""Get a summary of the current optimization state."""
return {
"iteration": self._iteration,
"n_observations": self.prior_manager.data_prior.n_observations,
"prior_summary": self.prior_manager.summary(),
"model_quality": self.model_quality(),
"parameter_space": {
"dimension": self.parameter_space.dimension,
"parameters": self.parameter_space.parameter_names,
},
}
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