Atomizer/docs/06_PROTOCOLS_DETAILED/protocol_13_implementation_guide.md

Implementation Guide: Protocol 13 - Real-Time Tracking

Date: 2025-11-21 Status: 🚧 IN PROGRESS Priority: P0 - CRITICAL

What's Done ✅

1. Created realtime_tracking.py

   • RealtimeTrackingCallback class
   • Writes JSON files after EVERY trial (atomic writes)
   • Files: optimizer_state.json, strategy_history.json, trial_log.json, landscape_snapshot.json, confidence_history.json

2. Fixed Multi-Objective Strategy (Protocol 12)

   • Modified strategy_selector.py
   • Added _recommend_multiobjective_strategy() method
   • Multi-objective: Random (8 trials) → TPE with multivariate

What's Needed ⚠️

Step 1: Integrate Callback into IntelligentOptimizer

File: optimization_engine/intelligent_optimizer.py

Line 48 - Add import:

from optimization_engine.adaptive_characterization import CharacterizationStoppingCriterion
from optimization_engine.realtime_tracking import create_realtime_callback  # ADD THIS

Line ~90 in __init__() - Create callback:

def __init__(self, study_name: str, study_dir: Path, config: Dict, verbose: bool = True):
    # ... existing init code ...

    # Create realtime tracking callback (Protocol 13)
    self.realtime_callback = create_realtime_callback(
        tracking_dir=self.tracking_dir,
        optimizer_ref=self,
        verbose=self.verbose
    )

Find ALL study.optimize() calls and add callback:

Search for: self.study.optimize(

Replace pattern:

# BEFORE:
self.study.optimize(objective_function, n_trials=check_interval)

# AFTER:
self.study.optimize(
    objective_function,
    n_trials=check_interval,
    callbacks=[self.realtime_callback]
)

Locations to fix (approximate line numbers):

• Line ~190: Characterization phase
• Line ~230: Optimization phase (multiple locations)
• Line ~260: Refinement phase
• Line ~380: Fallback optimization

CRITICAL: EVERY study.optimize() call must include callbacks=[self.realtime_callback]

Step 2: Test Realtime Tracking

# Clear old results
cd studies/bracket_stiffness_optimization_V2
del /Q 2_results\study.db
rd /S /Q 2_results\intelligent_optimizer

# Run with new code
python -B run_optimization.py --trials 10

# Verify files appear IMMEDIATELY after each trial
dir 2_results\intelligent_optimizer
# Should see:
# - optimizer_state.json
# - strategy_history.json
# - trial_log.json
# - landscape_snapshot.json
# - confidence_history.json

# Check file updates in real-time
python -c "import json; print(json.load(open('2_results/intelligent_optimizer/trial_log.json'))[-1])"

---

Dashboard Implementation Plan

Backend API Endpoints (Python/FastAPI)

File: atomizer-dashboard/backend/api/routes/optimization.py

Add new endpoints:

@router.get("/studies/{study_id}/metadata")
async def get_study_metadata(study_id: str):
    """Read optimization_config.json for objectives, design vars, units."""
    study_dir = find_study_dir(study_id)
    config_file = study_dir / "optimization_config.json"

    with open(config_file) as f:
        config = json.load(f)

    return {
        "objectives": config["objectives"],
        "design_variables": config["design_variables"],
        "constraints": config.get("constraints", []),
        "study_name": config["study_name"]
    }

@router.get("/studies/{study_id}/optimizer-state")
async def get_optimizer_state(study_id: str):
    """Read realtime optimizer state from intelligent_optimizer/."""
    study_dir = find_study_dir(study_id)
    state_file = study_dir / "2_results/intelligent_optimizer/optimizer_state.json"

    if not state_file.exists():
        return {"available": False}

    with open(state_file) as f:
        state = json.load(f)

    return {"available": True, **state}

@router.get("/studies/{study_id}/pareto-front")
async def get_pareto_front(study_id: str):
    """Get Pareto-optimal solutions for multi-objective studies."""
    study_dir = find_study_dir(study_id)
    db_path = study_dir / "2_results/study.db"

    storage = optuna.storages.RDBStorage(f"sqlite:///{db_path}")
    study = optuna.load_study(study_name=study_id, storage=storage)

    if len(study.directions) == 1:
        return {"is_multi_objective": False}

    pareto_trials = study.best_trials

    return {
        "is_multi_objective": True,
        "pareto_front": [
            {
                "trial_number": t.number,
                "values": t.values,
                "params": t.params,
                "user_attrs": dict(t.user_attrs)
            }
            for t in pareto_trials
        ]
    }

Frontend Components (React/TypeScript)

1. Optimizer Panel Component

File: atomizer-dashboard/frontend/src/components/OptimizerPanel.tsx (CREATE NEW)

import { useEffect, useState } from 'react';
import { Card } from './Card';

interface OptimizerState {
  available: boolean;
  current_phase?: string;
  current_strategy?: string;
  trial_number?: number;
  total_trials?: number;
  latest_recommendation?: {
    strategy: string;
    confidence: number;
    reasoning: string;
  };
}

export function OptimizerPanel({ studyId }: { studyId: string }) {
  const [state, setState] = useState<OptimizerState | null>(null);

  useEffect(() => {
    const fetchState = async () => {
      const res = await fetch(`/api/optimization/studies/${studyId}/optimizer-state`);
      const data = await res.json();
      setState(data);
    };

    fetchState();
    const interval = setInterval(fetchState, 1000); // Update every second
    return () => clearInterval(interval);
  }, [studyId]);

  if (!state?.available) {
    return null;
  }

  return (
    <Card title="Intelligent Optimizer Status">
      <div className="space-y-4">
        {/* Phase */}
        <div>
          <div className="text-sm text-dark-300">Phase</div>
          <div className="text-lg font-semibold text-primary-400">
            {state.current_phase || 'Unknown'}
          </div>
        </div>

        {/* Strategy */}
        <div>
          <div className="text-sm text-dark-300">Current Strategy</div>
          <div className="text-lg font-semibold text-blue-400">
            {state.current_strategy?.toUpperCase() || 'Unknown'}
          </div>
        </div>

        {/* Progress */}
        <div>
          <div className="text-sm text-dark-300">Progress</div>
          <div className="text-lg">
            {state.trial_number} / {state.total_trials} trials
          </div>
          <div className="w-full bg-dark-500 rounded-full h-2 mt-2">
            <div
              className="bg-primary-400 h-2 rounded-full transition-all"
              style={{
                width: `${((state.trial_number || 0) / (state.total_trials || 1)) * 100}%`
              }}
            />
          </div>
        </div>

        {/* Confidence */}
        {state.latest_recommendation && (
          <div>
            <div className="text-sm text-dark-300">Confidence</div>
            <div className="flex items-center gap-2">
              <div className="flex-1 bg-dark-500 rounded-full h-2">
                <div
                  className="bg-green-400 h-2 rounded-full transition-all"
                  style={{
                    width: `${state.latest_recommendation.confidence * 100}%`
                  }}
                />
              </div>
              <span className="text-sm font-mono">
                {(state.latest_recommendation.confidence * 100).toFixed(0)}%
              </span>
            </div>
          </div>
        )}

        {/* Reasoning */}
        {state.latest_recommendation && (
          <div>
            <div className="text-sm text-dark-300">Reasoning</div>
            <div className="text-sm text-dark-100 mt-1">
              {state.latest_recommendation.reasoning}
            </div>
          </div>
        )}
      </div>
    </Card>
  );
}

2. Pareto Front Plot

File: atomizer-dashboard/frontend/src/components/ParetoPlot.tsx (CREATE NEW)

import { ScatterChart, Scatter, XAxis, YAxis, CartesianGrid, Tooltip, Cell, ResponsiveContainer } from 'recharts';

interface ParetoData {
  trial_number: number;
  values: [number, number];
  params: Record<string, number>;
  constraint_satisfied?: boolean;
}

export function ParetoPlot({ paretoData, objectives }: {
  paretoData: ParetoData[];
  objectives: Array<{ name: string; unit?: string }>;
}) {
  if (paretoData.length === 0) {
    return (
      <div className="h-64 flex items-center justify-center text-dark-300">
        No Pareto front data yet
      </div>
    );
  }

  const data = paretoData.map(trial => ({
    x: trial.values[0],
    y: trial.values[1],
    trial_number: trial.number,
    feasible: trial.constraint_satisfied !== false
  }));

  return (
    <ResponsiveContainer width="100%" height={400}>
      <ScatterChart>
        <CartesianGrid strokeDasharray="3 3" stroke="#334155" />
        <XAxis
          type="number"
          dataKey="x"
          name={objectives[0]?.name || 'Objective 1'}
          stroke="#94a3b8"
          label={{
            value: `${objectives[0]?.name || 'Objective 1'} ${objectives[0]?.unit || ''}`.trim(),
            position: 'insideBottom',
            offset: -5,
            fill: '#94a3b8'
          }}
        />
        <YAxis
          type="number"
          dataKey="y"
          name={objectives[1]?.name || 'Objective 2'}
          stroke="#94a3b8"
          label={{
            value: `${objectives[1]?.name || 'Objective 2'} ${objectives[1]?.unit || ''}`.trim(),
            angle: -90,
            position: 'insideLeft',
            fill: '#94a3b8'
          }}
        />
        <Tooltip
          contentStyle={{ backgroundColor: '#1e293b', border: 'none', borderRadius: '8px' }}
          labelStyle={{ color: '#e2e8f0' }}
        />
        <Scatter name="Pareto Front" data={data}>
          {data.map((entry, index) => (
            <Cell
              key={`cell-${index}`}
              fill={entry.feasible ? '#10b981' : '#ef4444'}
              r={entry.feasible ? 6 : 4}
            />
          ))}
        </Scatter>
      </ScatterChart>
    </ResponsiveContainer>
  );
}

3. Update Dashboard.tsx

File: atomizer-dashboard/frontend/src/pages/Dashboard.tsx

Add imports at top:

import { OptimizerPanel } from '../components/OptimizerPanel';
import { ParetoPlot } from '../components/ParetoPlot';

Add new state:

const [studyMetadata, setStudyMetadata] = useState(null);
const [paretoFront, setParetoFront] = useState([]);

Fetch metadata when study selected:

useEffect(() => {
  if (selectedStudyId) {
    fetch(`/api/optimization/studies/${selectedStudyId}/metadata`)
      .then(res => res.json())
      .then(setStudyMetadata);

    fetch(`/api/optimization/studies/${selectedStudyId}/pareto-front`)
      .then(res => res.json())
      .then(data => {
        if (data.is_multi_objective) {
          setParetoFront(data.pareto_front);
        }
      });
  }
}, [selectedStudyId]);

Add components to layout:

{/* Add after metrics grid */}
<div className="grid grid-cols-2 gap-6 mb-6">
  <OptimizerPanel studyId={selectedStudyId} />
  {paretoFront.length > 0 && (
    <Card title="Pareto Front">
      <ParetoPlot
        paretoData={paretoFront}
        objectives={studyMetadata?.objectives || []}
      />
    </Card>
  )}
</div>

---

Testing Checklist

• Realtime callback writes files after EVERY trial
• optimizer_state.json updates in real-time
• Dashboard shows optimizer panel with live updates
• Pareto front appears for multi-objective studies
• Units are dynamic (read from config)
• Multi-objective strategy switches from random → TPE after 8 trials

---

Next Steps

1. Integrate callback into IntelligentOptimizer (Steps above)
2. Implement backend API endpoints
3. Create frontend components
4. Test end-to-end with bracket study
5. Document as Protocol 13