feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements
## Protocol 13: Adaptive Multi-Objective Optimization - Iterative FEA + Neural Network surrogate workflow - Initial FEA sampling, NN training, NN-accelerated search - FEA validation of top NN predictions, retraining loop - adaptive_state.json tracks iteration history and best values - M1 mirror study (V11) with 103 FEA, 3000 NN trials ## Dashboard Visualization Enhancements - Added Plotly.js interactive charts (parallel coords, Pareto, convergence) - Lazy loading with React.lazy() for performance - Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB) - Chart library toggle (Recharts default, Plotly on-demand) - ExpandableChart component for full-screen modal views - ConsoleOutput component for real-time log viewing ## Documentation - Protocol 13 detailed documentation - Dashboard visualization guide - Plotly components README - Updated run-optimization skill with Mode 5 (adaptive) ## Bug Fixes - Fixed TypeScript errors in dashboard components - Fixed Card component to accept ReactNode title - Removed unused imports across components 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Antoine
@@ -287,18 +287,21 @@ async def get_optimization_history(study_id: str, limit: Optional[int] = None):
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conn = sqlite3.connect(str(study_db))
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cursor = conn.cursor()
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# Get all completed trials
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# Get all completed trials FROM ALL STUDIES in the database
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# This handles adaptive optimizations that create multiple Optuna studies
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# (e.g., v11_fea for FEA trials, v11_iter1_nn for NN trials, etc.)
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cursor.execute("""
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SELECT trial_id, number, datetime_start, datetime_complete
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FROM trials
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WHERE state = 'COMPLETE'
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ORDER BY number DESC
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SELECT t.trial_id, t.number, t.datetime_start, t.datetime_complete, s.study_name
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FROM trials t
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JOIN studies s ON t.study_id = s.study_id
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WHERE t.state = 'COMPLETE'
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ORDER BY t.datetime_start DESC
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""" + (f" LIMIT {limit}" if limit else ""))
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trial_rows = cursor.fetchall()
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trials = []
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for trial_id, trial_num, start_time, end_time in trial_rows:
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for trial_id, trial_num, start_time, end_time, study_name in trial_rows:
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# Get objectives for this trial
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cursor.execute("""
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SELECT value
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@@ -363,15 +366,24 @@ async def get_optimization_history(study_id: str, limit: Optional[int] = None):
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# Merge with params (prefer user_attrs design_vars if available)
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final_design_vars = {**params, **design_vars_from_attrs} if design_vars_from_attrs else params
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# Extract source for FEA vs NN differentiation
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source = user_attrs.get("source", "FEA") # Default to FEA for legacy studies
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# Use trial_id as unique identifier when multiple Optuna studies exist
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# This avoids trial number collisions between studies
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unique_trial_num = trial_id if study_name else trial_num
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trials.append({
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"trial_number": trial_num,
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"trial_number": unique_trial_num,
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"objective": values[0] if len(values) > 0 else None, # Primary objective
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"objectives": values if len(values) > 1 else None, # All objectives for multi-objective
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"design_variables": final_design_vars, # Use merged design vars
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"results": results,
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"user_attrs": user_attrs, # Include all user attributes
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"source": source, # FEA or NN
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"start_time": start_time,
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"end_time": end_time
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"end_time": end_time,
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"study_name": study_name # Include for debugging
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})
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conn.close()
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@@ -910,6 +922,72 @@ async def download_report(study_id: str, filename: str):
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raise HTTPException(status_code=500, detail=f"Failed to download report: {str(e)}")
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@router.get("/studies/{study_id}/console")
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async def get_console_output(study_id: str, lines: int = 200):
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"""
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Get the latest console output/logs from the optimization run
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Args:
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study_id: Study identifier
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lines: Number of lines to return (default: 200)
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Returns:
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JSON with console output lines
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"""
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try:
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study_dir = STUDIES_DIR / study_id
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if not study_dir.exists():
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raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
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# Look for log files in various locations
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log_paths = [
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study_dir / "optimization.log",
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study_dir / "2_results" / "optimization.log",
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study_dir / "3_results" / "optimization.log",
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study_dir / "run.log",
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]
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log_content = None
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log_path_used = None
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for log_path in log_paths:
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if log_path.exists():
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log_path_used = log_path
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break
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if log_path_used is None:
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return {
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"lines": [],
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"total_lines": 0,
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"log_file": None,
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"message": "No log file found. Optimization may not have started yet."
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}
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# Read the last N lines efficiently
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with open(log_path_used, 'r', encoding='utf-8', errors='replace') as f:
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all_lines = f.readlines()
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# Get last N lines
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last_lines = all_lines[-lines:] if len(all_lines) > lines else all_lines
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# Clean up lines (remove trailing newlines)
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last_lines = [line.rstrip('\n\r') for line in last_lines]
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return {
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"lines": last_lines,
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"total_lines": len(all_lines),
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"displayed_lines": len(last_lines),
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"log_file": str(log_path_used),
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"timestamp": datetime.now().isoformat()
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}
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except HTTPException:
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raise
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Failed to read console output: {str(e)}")
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@router.get("/studies/{study_id}/report")
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async def get_study_report(study_id: str):
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"""
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