Atomizer/docs/archive/historical/STUDY_CONTINUATION_STANDARD.md

Study Continuation - Atomizer Standard Feature

Date: November 20, 2025 Status: ✅ Implemented as Standard Feature

---

Overview

Study continuation is now a standardized Atomizer feature for dashboard integration. It provides a clean API for continuing existing optimization studies with additional trials.

Previously, continuation was improvised on-demand. Now it's a first-class feature alongside "Start New Optimization".

---

Module

optimization_engine/study_continuation.py

---

API

Main Function: continue_study()

from optimization_engine.study_continuation import continue_study

results = continue_study(
    study_dir=Path("studies/my_study"),
    additional_trials=50,
    objective_function=my_objective,
    design_variables={'param1': (0, 10), 'param2': (0, 100)},
    target_value=115.0,
    tolerance=0.1,
    verbose=True
)

Returns:

{
    'study': optuna.Study,           # The study object
    'total_trials': 100,              # Total after continuation
    'successful_trials': 95,          # Completed trials
    'pruned_trials': 5,               # Failed trials
    'best_value': 0.05,              # Best objective value
    'best_params': {...},            # Best parameters
    'target_achieved': True          # If target specified
}

Utility Functions

can_continue_study()

Check if a study is ready for continuation:

from optimization_engine.study_continuation import can_continue_study

can_continue, message = can_continue_study(Path("studies/my_study"))

if can_continue:
    print(f"Ready: {message}")
    # message: "Study 'my_study' ready (current trials: 50)"
else:
    print(f"Cannot continue: {message}")
    # message: "No study.db found. Run initial optimization first."

get_study_status()

Get current study information:

from optimization_engine.study_continuation import get_study_status

status = get_study_status(Path("studies/my_study"))

if status:
    print(f"Study: {status['study_name']}")
    print(f"Trials: {status['total_trials']}")
    print(f"Success rate: {status['successful_trials']/status['total_trials']*100:.1f}%")
    print(f"Best: {status['best_value']}")
else:
    print("Study not found or invalid")

Returns:

{
    'study_name': 'my_study',
    'total_trials': 50,
    'successful_trials': 47,
    'pruned_trials': 3,
    'pruning_rate': 0.06,
    'best_value': 0.42,
    'best_params': {'param1': 5.2, 'param2': 78.3}
}

---

Dashboard Integration

UI Workflow

When user selects a study in the dashboard:

1. User clicks on study → Dashboard calls get_study_status()

2. Dashboard shows study info card:
   ┌──────────────────────────────────────┐
   │ Study: circular_plate_test           │
   │ Current Trials: 50                   │
   │ Success Rate: 94%                    │
   │ Best Result: 0.42 Hz error           │
   │                                      │
   │ [Continue Study]  [View Results]     │
   └──────────────────────────────────────┘

3. User clicks "Continue Study" → Shows form:
   ┌──────────────────────────────────────┐
   │ Continue Optimization                │
   │                                      │
   │ Additional Trials: [50]              │
   │ Target Value (optional): [115.0]     │
   │ Tolerance (optional): [0.1]          │
   │                                      │
   │ [Cancel]              [Start]        │
   └──────────────────────────────────────┘

4. User clicks "Start" → Dashboard calls continue_study()

5. Progress shown in real-time (like initial optimization)

Example Dashboard Code

from pathlib import Path
from optimization_engine.study_continuation import (
    get_study_status,
    can_continue_study,
    continue_study
)

def show_study_panel(study_dir: Path):
    """Display study panel with continuation option."""

    # Get current status
    status = get_study_status(study_dir)

    if not status:
        print("Study not found or incomplete")
        return

    # Show study info
    print(f"Study: {status['study_name']}")
    print(f"Current Trials: {status['total_trials']}")
    print(f"Best Result: {status['best_value']:.4f}")

    # Check if can continue
    can_continue, message = can_continue_study(study_dir)

    if can_continue:
        # Enable "Continue" button
        print("✓ Ready to continue")
    else:
        # Disable "Continue" button, show reason
        print(f"✗ Cannot continue: {message}")


def handle_continue_button_click(study_dir: Path, additional_trials: int):
    """Handle user clicking 'Continue Study' button."""

    # Load the objective function for this study
    # (Dashboard needs to reconstruct this from study config)
    from studies.my_study.run_optimization import objective

    # Continue the study
    results = continue_study(
        study_dir=study_dir,
        additional_trials=additional_trials,
        objective_function=objective,
        verbose=True  # Stream output to dashboard
    )

    # Show completion notification
    if results.get('target_achieved'):
        notify_user(f"Target achieved! Best: {results['best_value']:.4f}")
    else:
        notify_user(f"Completed {additional_trials} trials. Best: {results['best_value']:.4f}")

---

Comparison: Old vs New

Before (Improvised)

Each study needed a custom continue_optimization.py:

studies/my_study/
├── run_optimization.py          # Standard (from protocol)
├── continue_optimization.py     # Improvised (custom for each study)
└── 2_results/
    └── study.db

Problems:

• Not standardized across studies
• Manual creation required
• No dashboard integration possible
• Inconsistent behavior

After (Standardized)

All studies use the same continuation API:

studies/my_study/
├── run_optimization.py          # Standard (from protocol)
└── 2_results/
    └── study.db

# No continue_optimization.py needed!
# Just call continue_study() from anywhere

Benefits:

• ✅ Standardized behavior
• ✅ Dashboard-ready API
• ✅ Consistent across all studies
• ✅ No per-study custom code

---

Usage Examples

Example 1: Simple Continuation

from pathlib import Path
from optimization_engine.study_continuation import continue_study
from studies.my_study.run_optimization import objective

# Continue with 50 more trials
results = continue_study(
    study_dir=Path("studies/my_study"),
    additional_trials=50,
    objective_function=objective
)

print(f"New best: {results['best_value']}")

Example 2: With Target Checking

# Continue until target is met or 100 additional trials
results = continue_study(
    study_dir=Path("studies/circular_plate_test"),
    additional_trials=100,
    objective_function=objective,
    target_value=115.0,
    tolerance=0.1
)

if results['target_achieved']:
    print(f"Success! Achieved in {results['total_trials']} total trials")
else:
    print(f"Target not reached. Best: {results['best_value']}")

Example 3: Dashboard Batch Processing

from pathlib import Path
from optimization_engine.study_continuation import get_study_status

# Find all studies that can be continued
studies_dir = Path("studies")

for study_dir in studies_dir.iterdir():
    if not study_dir.is_dir():
        continue

    status = get_study_status(study_dir)

    if status and status['pruning_rate'] > 0.10:
        print(f"⚠️  {status['study_name']}: High pruning rate ({status['pruning_rate']*100:.1f}%)")
        print(f"   Consider investigating before continuing")
    elif status:
        print(f"✓ {status['study_name']}: {status['total_trials']} trials, best={status['best_value']:.4f}")

---

File Structure

Standard Study Directory

studies/my_study/
├── 1_setup/
│   ├── model/                   # FEA model files
│   ├── workflow_config.json     # Contains study_name
│   └── optimization_config.json
├── 2_results/
│   ├── study.db                # Optuna database (required for continuation)
│   ├── optimization_history_incremental.json
│   └── intelligent_optimizer/
└── 3_reports/
    └── OPTIMIZATION_REPORT.md

Required for Continuation:

• 1_setup/workflow_config.json (contains study_name)
• 2_results/study.db (Optuna database with trial data)

---

Error Handling

The API provides clear error messages:

# Study doesn't exist
can_continue_study(Path("studies/nonexistent"))
# Returns: (False, "No workflow_config.json found in studies/nonexistent/1_setup")

# Study exists but not run yet
can_continue_study(Path("studies/new_study"))
# Returns: (False, "No study.db found. Run initial optimization first.")

# Study database corrupted
can_continue_study(Path("studies/bad_study"))
# Returns: (False, "Study 'bad_study' not found in database")

# Study has no trials
can_continue_study(Path("studies/empty_study"))
# Returns: (False, "Study exists but has no trials yet")

---

Dashboard Buttons

Two Standard Actions

Every study in the dashboard should have:

1. "Start New Optimization" → Calls run_optimization.py

   • Requires: Study setup complete
   • Creates: Fresh study database
   • Use when: Starting from scratch

2. "Continue Study" → Calls continue_study()

   • Requires: Existing study.db with trials
   • Preserves: All existing trial data
   • Use when: Adding more iterations

Both are now standardized Atomizer features.

---

Testing

Test the continuation API:

# Test status check
python -c "
from pathlib import Path
from optimization_engine.study_continuation import get_study_status

status = get_study_status(Path('studies/circular_plate_protocol10_v2_1_test'))
if status:
    print(f\"Study: {status['study_name']}\")
    print(f\"Trials: {status['total_trials']}\")
    print(f\"Best: {status['best_value']}\")
"

# Test continuation check
python -c "
from pathlib import Path
from optimization_engine.study_continuation import can_continue_study

can_continue, msg = can_continue_study(Path('studies/circular_plate_protocol10_v2_1_test'))
print(f\"Can continue: {can_continue}\")
print(f\"Message: {msg}\")
"

---

Summary

Feature	Before	After
Implementation	Improvised per study	Standardized module
Dashboard integration	Not possible	Full API support
Consistency	Varies by study	Uniform behavior
Error handling	Manual	Built-in with messages
Study status	Manual queries	get_study_status()
Continuation check	Manual	can_continue_study()

Status: ✅ Ready for dashboard integration

Module: optimization_engine/study_continuation.py