feat: Pre-migration checkpoint - updated docs and utilities
Updates before optimization_engine migration: - Updated migration plan to v2.1 with complete file inventory - Added OP_07 disk optimization protocol - Added SYS_16 self-aware turbo protocol - Added study archiver and cleanup utilities - Added ensemble surrogate module - Updated NX solver and session manager - Updated zernike HTML generator - Added context engineering plan - LAC session insights updates 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
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optimization_engine/surrogates/__init__.py
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optimization_engine/surrogates/__init__.py
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"""
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Surrogate models for FEA acceleration.
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Available surrogates:
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- EnsembleSurrogate: Multiple MLPs with uncertainty quantification
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- OODDetector: Out-of-distribution detection
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"""
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from .ensemble_surrogate import (
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EnsembleSurrogate,
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OODDetector,
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create_and_train_ensemble
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)
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__all__ = [
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'EnsembleSurrogate',
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'OODDetector',
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'create_and_train_ensemble'
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]
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optimization_engine/surrogates/ensemble_surrogate.py
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optimization_engine/surrogates/ensemble_surrogate.py
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#!/usr/bin/env python3
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"""
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Ensemble Surrogate with Uncertainty Quantification
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Addresses the V5 failure mode where single MLPs gave overconfident predictions
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in out-of-distribution regions, leading L-BFGS to fake optima.
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Key features:
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1. Ensemble of N MLPs - disagreement = uncertainty
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2. OOD detection - reject predictions far from training data
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3. Confidence bounds - never trust point predictions alone
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4. Active learning - prioritize FEA in uncertain regions
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Author: Atomizer
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Created: 2025-12-28
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"""
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import numpy as np
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from typing import Tuple, List, Dict, Optional
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from pathlib import Path
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import json
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import logging
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logger = logging.getLogger(__name__)
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try:
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import torch
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import torch.nn as nn
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HAS_TORCH = True
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except ImportError:
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HAS_TORCH = False
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logger.warning("PyTorch not available - ensemble features limited")
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from sklearn.neighbors import NearestNeighbors
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class MLP(nn.Module):
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"""Single MLP for ensemble."""
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def __init__(self, input_dim: int, output_dim: int, hidden_dims: List[int] = None):
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super().__init__()
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hidden_dims = hidden_dims or [64, 32]
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layers = []
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in_dim = input_dim
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for h_dim in hidden_dims:
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layers.append(nn.Linear(in_dim, h_dim))
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layers.append(nn.ReLU())
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layers.append(nn.Dropout(0.1))
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in_dim = h_dim
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layers.append(nn.Linear(in_dim, output_dim))
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self.net = nn.Sequential(*layers)
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def forward(self, x):
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return self.net(x)
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class OODDetector:
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"""
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Out-of-Distribution detector using multiple methods:
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1. Z-score check (is input within N std of training mean)
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2. KNN distance (is input close to training points)
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"""
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def __init__(self, X_train: np.ndarray, z_threshold: float = 3.0, knn_k: int = 5):
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self.X_train = X_train
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self.z_threshold = z_threshold
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self.knn_k = knn_k
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# Compute training statistics
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self.mean = X_train.mean(axis=0)
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self.std = X_train.std(axis=0) + 1e-8
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# Fit KNN for local density estimation
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self.knn = NearestNeighbors(n_neighbors=min(knn_k, len(X_train)))
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self.knn.fit(X_train)
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# Compute typical KNN distances in training set
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train_distances, _ = self.knn.kneighbors(X_train)
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self.typical_knn_dist = np.median(train_distances.mean(axis=1))
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logger.info(f"[OOD] Initialized with {len(X_train)} training points")
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logger.info(f"[OOD] Typical KNN distance: {self.typical_knn_dist:.4f}")
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def z_score_check(self, x: np.ndarray) -> Tuple[bool, float]:
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"""Check if point is within z_threshold std of training mean."""
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x = np.atleast_2d(x)
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z_scores = np.abs((x - self.mean) / self.std)
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max_z = z_scores.max(axis=1)
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is_ok = max_z < self.z_threshold
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return is_ok[0] if len(is_ok) == 1 else is_ok, max_z[0] if len(max_z) == 1 else max_z
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def knn_distance_check(self, x: np.ndarray) -> Tuple[bool, float]:
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"""Check if point is close enough to training data."""
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x = np.atleast_2d(x)
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distances, _ = self.knn.kneighbors(x)
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avg_dist = distances.mean(axis=1)
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# Allow up to 3x typical distance
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is_ok = avg_dist < 3 * self.typical_knn_dist
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return is_ok[0] if len(is_ok) == 1 else is_ok, avg_dist[0] if len(avg_dist) == 1 else avg_dist
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def is_in_distribution(self, x: np.ndarray) -> Tuple[bool, Dict]:
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"""Combined OOD check."""
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z_ok, z_score = self.z_score_check(x)
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knn_ok, knn_dist = self.knn_distance_check(x)
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is_ok = z_ok and knn_ok
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details = {
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'z_score': float(z_score),
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'z_ok': bool(z_ok),
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'knn_dist': float(knn_dist),
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'knn_ok': bool(knn_ok),
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'in_distribution': bool(is_ok)
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}
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return is_ok, details
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class EnsembleSurrogate:
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"""
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Ensemble of MLPs with uncertainty quantification.
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Key insight: Models trained with different random seeds will agree
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in well-sampled regions but disagree in extrapolation regions.
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Disagreement = epistemic uncertainty.
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"""
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def __init__(
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self,
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input_dim: int,
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output_dim: int,
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n_models: int = 5,
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hidden_dims: List[int] = None,
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device: str = 'auto'
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):
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.n_models = n_models
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self.hidden_dims = hidden_dims or [64, 32]
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if device == 'auto':
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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else:
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self.device = torch.device(device)
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# Create ensemble
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self.models = [
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MLP(input_dim, output_dim, hidden_dims).to(self.device)
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for _ in range(n_models)
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]
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# Normalization stats
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self.x_mean = None
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self.x_std = None
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self.y_mean = None
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self.y_std = None
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# OOD detector
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self.ood_detector = None
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# Training state
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self.is_trained = False
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logger.info(f"[ENSEMBLE] Created {n_models} MLPs on {self.device}")
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def train(
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self,
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X: np.ndarray,
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Y: np.ndarray,
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epochs: int = 500,
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lr: float = 0.001,
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val_split: float = 0.1,
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patience: int = 50
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) -> Dict:
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"""Train all models in ensemble with different random seeds."""
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# Compute normalization
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self.x_mean = X.mean(axis=0)
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self.x_std = X.std(axis=0) + 1e-8
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self.y_mean = Y.mean(axis=0)
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self.y_std = Y.std(axis=0) + 1e-8
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X_norm = (X - self.x_mean) / self.x_std
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Y_norm = (Y - self.y_mean) / self.y_std
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# Split data
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n_val = max(int(len(X) * val_split), 5)
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indices = np.random.permutation(len(X))
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val_idx, train_idx = indices[:n_val], indices[n_val:]
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X_train, Y_train = X_norm[train_idx], Y_norm[train_idx]
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X_val, Y_val = X_norm[val_idx], Y_norm[val_idx]
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# Convert to tensors
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X_t = torch.FloatTensor(X_train).to(self.device)
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Y_t = torch.FloatTensor(Y_train).to(self.device)
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X_v = torch.FloatTensor(X_val).to(self.device)
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Y_v = torch.FloatTensor(Y_val).to(self.device)
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# Train each model with different seed
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all_val_losses = []
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for i, model in enumerate(self.models):
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torch.manual_seed(42 + i * 1000) # Different init per model
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np.random.seed(42 + i * 1000)
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optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
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criterion = nn.MSELoss()
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best_val_loss = float('inf')
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patience_counter = 0
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best_state = None
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for epoch in range(epochs):
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# Train
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model.train()
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optimizer.zero_grad()
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pred = model(X_t)
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loss = criterion(pred, Y_t)
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loss.backward()
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optimizer.step()
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# Validate
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model.eval()
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with torch.no_grad():
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val_pred = model(X_v)
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val_loss = criterion(val_pred, Y_v).item()
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# Early stopping
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if val_loss < best_val_loss:
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best_val_loss = val_loss
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patience_counter = 0
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best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}
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else:
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patience_counter += 1
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if patience_counter >= patience:
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break
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# Restore best
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if best_state:
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model.load_state_dict(best_state)
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model.to(self.device)
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all_val_losses.append(best_val_loss)
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logger.info(f"[ENSEMBLE] Model {i+1}/{self.n_models} trained, val_loss={best_val_loss:.4f}")
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# Initialize OOD detector
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self.ood_detector = OODDetector(X_norm)
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self.is_trained = True
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# Compute ensemble metrics
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metrics = self._compute_metrics(X_val, Y_val)
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metrics['val_losses'] = all_val_losses
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return metrics
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def _compute_metrics(self, X_val: np.ndarray, Y_val: np.ndarray) -> Dict:
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"""Compute R², MAE, and ensemble disagreement on validation set."""
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mean, std = self.predict_normalized(X_val)
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# R² for each output
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ss_res = np.sum((Y_val - mean) ** 2, axis=0)
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ss_tot = np.sum((Y_val - Y_val.mean(axis=0)) ** 2, axis=0)
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r2 = 1 - ss_res / (ss_tot + 1e-8)
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# MAE
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mae = np.abs(Y_val - mean).mean(axis=0)
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# Average ensemble disagreement
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avg_std = std.mean()
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return {
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'r2': r2.tolist(),
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'mae': mae.tolist(),
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'avg_ensemble_std': float(avg_std),
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'n_val': len(X_val)
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}
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def predict_normalized(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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"""Predict on normalized inputs, return normalized outputs."""
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X = np.atleast_2d(X)
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X_t = torch.FloatTensor(X).to(self.device)
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preds = []
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for model in self.models:
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model.eval()
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with torch.no_grad():
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pred = model(X_t).cpu().numpy()
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preds.append(pred)
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preds = np.array(preds) # (n_models, n_samples, n_outputs)
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mean = preds.mean(axis=0)
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std = preds.std(axis=0)
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return mean, std
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def predict(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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"""
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Predict with uncertainty.
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Returns:
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mean: (n_samples, n_outputs) predicted values
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std: (n_samples, n_outputs) uncertainty (ensemble disagreement)
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"""
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X = np.atleast_2d(X)
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# Normalize input
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X_norm = (X - self.x_mean) / self.x_std
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# Get predictions
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mean_norm, std_norm = self.predict_normalized(X_norm)
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# Denormalize
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mean = mean_norm * self.y_std + self.y_mean
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std = std_norm * self.y_std # Std scales with y_std
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return mean, std
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def predict_with_confidence(self, X: np.ndarray) -> Dict:
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"""
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Full prediction with confidence assessment.
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Returns dict with:
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- mean: predicted values
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- std: uncertainty
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- confidence: 0-1 score (higher = more reliable)
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- in_distribution: OOD check result
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- recommendation: 'trust', 'verify', or 'reject'
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"""
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X = np.atleast_2d(X)
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mean, std = self.predict(X)
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# OOD check
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X_norm = (X - self.x_mean) / self.x_std
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ood_results = [self.ood_detector.is_in_distribution(x) for x in X_norm]
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in_distribution = [r[0] for r in ood_results]
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# Compute confidence score (0 = no confidence, 1 = high confidence)
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# Based on: relative std (lower = better) and OOD (in = better)
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relative_std = std / (np.abs(mean) + 1e-6)
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avg_rel_std = relative_std.mean(axis=1)
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confidence = np.zeros(len(X))
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for i in range(len(X)):
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if not in_distribution[i]:
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confidence[i] = 0.0 # OOD = no confidence
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elif avg_rel_std[i] > 0.3:
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confidence[i] = 0.2 # High uncertainty
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elif avg_rel_std[i] > 0.1:
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confidence[i] = 0.5 # Medium uncertainty
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else:
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confidence[i] = 0.9 # Low uncertainty
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# Recommendations
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recommendations = []
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for i in range(len(X)):
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if confidence[i] >= 0.7:
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recommendations.append('trust')
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elif confidence[i] >= 0.3:
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recommendations.append('verify') # Run FEA to check
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else:
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recommendations.append('reject') # Don't use, run FEA instead
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return {
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'mean': mean,
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'std': std,
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'confidence': confidence,
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'in_distribution': in_distribution,
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'recommendation': recommendations
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}
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def acquisition_score(self, X: np.ndarray, best_so_far: float, xi: float = 0.01) -> np.ndarray:
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"""
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Expected Improvement acquisition function.
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High score = worth running FEA (either promising or uncertain).
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Args:
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X: candidate points
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best_so_far: current best objective value
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xi: exploration-exploitation tradeoff (higher = more exploration)
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Returns:
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scores: acquisition score per point
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"""
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X = np.atleast_2d(X)
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mean, std = self.predict(X)
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# For minimization: improvement = best - predicted
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# Take first objective (weighted sum) for acquisition
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if mean.ndim > 1:
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mean = mean[:, 0]
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std = std[:, 0]
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improvement = best_so_far - mean
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# Expected improvement with exploration bonus
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# Higher std = more exploration value
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z = improvement / (std + 1e-8)
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# Simple acquisition: exploitation + exploration
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scores = improvement + xi * std
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# Penalize OOD points
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X_norm = (X - self.x_mean) / self.x_std
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for i, x in enumerate(X_norm):
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is_ok, _ = self.ood_detector.is_in_distribution(x)
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if not is_ok:
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scores[i] *= 0.1 # Heavy penalty for OOD
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return scores
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def select_candidates_for_fea(
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self,
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candidates: np.ndarray,
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best_so_far: float,
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n_select: int = 5,
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diversity_weight: float = 0.3
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) -> Tuple[np.ndarray, np.ndarray]:
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"""
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Select diverse, high-acquisition candidates for FEA validation.
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Balances:
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1. High acquisition score (exploitation + exploration)
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2. Diversity (don't cluster all candidates together)
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3. In-distribution (avoid OOD predictions)
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Returns:
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selected: indices of selected candidates
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scores: acquisition scores
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"""
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scores = self.acquisition_score(candidates, best_so_far)
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# Greedy selection with diversity
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selected = []
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remaining = list(range(len(candidates)))
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while len(selected) < n_select and remaining:
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if not selected:
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# First: pick highest score
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best_idx = max(remaining, key=lambda i: scores[i])
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else:
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# Later: balance score with distance to selected
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def combined_score(i):
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# Min distance to already selected
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min_dist = min(
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np.linalg.norm(candidates[i] - candidates[j])
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for j in selected
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)
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||||
# Combine acquisition + diversity
|
||||
return scores[i] + diversity_weight * min_dist
|
||||
|
||||
best_idx = max(remaining, key=combined_score)
|
||||
|
||||
selected.append(best_idx)
|
||||
remaining.remove(best_idx)
|
||||
|
||||
return np.array(selected), scores[selected]
|
||||
|
||||
def save(self, path: Path):
|
||||
"""Save ensemble to disk."""
|
||||
path = Path(path)
|
||||
path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save each model
|
||||
for i, model in enumerate(self.models):
|
||||
torch.save(model.state_dict(), path / f"model_{i}.pt")
|
||||
|
||||
# Save normalization stats and config
|
||||
config = {
|
||||
'input_dim': self.input_dim,
|
||||
'output_dim': self.output_dim,
|
||||
'n_models': self.n_models,
|
||||
'hidden_dims': self.hidden_dims,
|
||||
'x_mean': self.x_mean.tolist() if self.x_mean is not None else None,
|
||||
'x_std': self.x_std.tolist() if self.x_std is not None else None,
|
||||
'y_mean': self.y_mean.tolist() if self.y_mean is not None else None,
|
||||
'y_std': self.y_std.tolist() if self.y_std is not None else None,
|
||||
}
|
||||
with open(path / "config.json", 'w') as f:
|
||||
json.dump(config, f, indent=2)
|
||||
|
||||
logger.info(f"[ENSEMBLE] Saved to {path}")
|
||||
|
||||
@classmethod
|
||||
def load(cls, path: Path, device: str = 'auto') -> 'EnsembleSurrogate':
|
||||
"""Load ensemble from disk."""
|
||||
path = Path(path)
|
||||
|
||||
with open(path / "config.json") as f:
|
||||
config = json.load(f)
|
||||
|
||||
surrogate = cls(
|
||||
input_dim=config['input_dim'],
|
||||
output_dim=config['output_dim'],
|
||||
n_models=config['n_models'],
|
||||
hidden_dims=config['hidden_dims'],
|
||||
device=device
|
||||
)
|
||||
|
||||
# Load normalization
|
||||
surrogate.x_mean = np.array(config['x_mean']) if config['x_mean'] else None
|
||||
surrogate.x_std = np.array(config['x_std']) if config['x_std'] else None
|
||||
surrogate.y_mean = np.array(config['y_mean']) if config['y_mean'] else None
|
||||
surrogate.y_std = np.array(config['y_std']) if config['y_std'] else None
|
||||
|
||||
# Load models
|
||||
for i, model in enumerate(surrogate.models):
|
||||
model.load_state_dict(torch.load(path / f"model_{i}.pt", map_location=surrogate.device))
|
||||
model.to(surrogate.device)
|
||||
|
||||
surrogate.is_trained = True
|
||||
logger.info(f"[ENSEMBLE] Loaded from {path}")
|
||||
|
||||
return surrogate
|
||||
|
||||
|
||||
# Convenience function for quick usage
|
||||
def create_and_train_ensemble(
|
||||
X: np.ndarray,
|
||||
Y: np.ndarray,
|
||||
n_models: int = 5,
|
||||
epochs: int = 500
|
||||
) -> EnsembleSurrogate:
|
||||
"""Create and train an ensemble surrogate."""
|
||||
surrogate = EnsembleSurrogate(
|
||||
input_dim=X.shape[1],
|
||||
output_dim=Y.shape[1] if Y.ndim > 1 else 1,
|
||||
n_models=n_models
|
||||
)
|
||||
|
||||
if Y.ndim == 1:
|
||||
Y = Y.reshape(-1, 1)
|
||||
|
||||
metrics = surrogate.train(X, Y, epochs=epochs)
|
||||
logger.info(f"[ENSEMBLE] Training complete: R²={metrics['r2']}, avg_std={metrics['avg_ensemble_std']:.4f}")
|
||||
|
||||
return surrogate
|
||||
Reference in New Issue
Block a user