Atomizer/optimization_engine/reporting/report_generator.py

"""
Generate human-readable optimization reports from incremental history JSON.

This script should be run automatically at the end of optimization, or manually
to generate a report for any completed optimization study.
"""

import json
import sys
from pathlib import Path
from typing import Dict, Any, List
import numpy as np


def generate_optimization_report(history_file: Path, target_value: float = None, tolerance: float = 0.1) -> str:
    """
    Generate a comprehensive human-readable optimization report.

    Args:
        history_file: Path to optimization_history_incremental.json
        target_value: Target objective value (if applicable)
        tolerance: Acceptable tolerance for success (default 0.1)

    Returns:
        Report text as a string
    """
    # Load history
    with open(history_file) as f:
        history = json.load(f)

    if not history:
        return "No optimization history found."

    report = []
    report.append('=' * 80)
    report.append('OPTIMIZATION REPORT')
    report.append('=' * 80)
    report.append('')

    # Study information
    study_dir = history_file.parent.parent.parent
    study_name = study_dir.name
    report.append('STUDY INFORMATION')
    report.append('-' * 80)
    report.append(f'Study: {study_name}')
    report.append(f'Total trials: {len(history)}')
    report.append('')

    # Design variables
    first_trial = history[0]
    design_vars = list(first_trial['design_variables'].keys())
    report.append('DESIGN VARIABLES')
    report.append('-' * 80)
    for var in design_vars:
        values = [t['design_variables'][var] for t in history]
        report.append(f'  {var}:')
        report.append(f'    Range: {min(values):.4f} - {max(values):.4f}')
        report.append(f'    Mean: {np.mean(values):.4f}')
    report.append('')

    # Objective results
    results = list(first_trial['results'].keys())
    report.append('OBJECTIVE RESULTS')
    report.append('-' * 80)
    for result in results:
        values = [t['results'][result] for t in history]
        report.append(f'  {result}:')
        report.append(f'    Range: {min(values):.4f} - {max(values):.4f}')
        report.append(f'    Mean: {np.mean(values):.4f}')
        report.append(f'    Std dev: {np.std(values):.4f}')
    report.append('')

    # Best trial
    objectives = [t['objective'] for t in history]
    best_trial = history[np.argmin(objectives)]

    report.append('BEST TRIAL')
    report.append('-' * 80)
    report.append(f'Trial #{best_trial["trial_number"]}')
    report.append(f'  Objective value: {best_trial["objective"]:.4f}')
    report.append('  Design variables:')
    for var, value in best_trial['design_variables'].items():
        report.append(f'    {var}: {value:.4f}')
    report.append('  Results:')
    for result, value in best_trial['results'].items():
        report.append(f'    {result}: {value:.4f}')
    report.append('')

    # Top 5 trials
    report.append('TOP 5 TRIALS (by objective value)')
    report.append('-' * 80)
    sorted_history = sorted(history, key=lambda x: x['objective'])
    for i, trial in enumerate(sorted_history[:5], 1):
        report.append(f'{i}. Trial #{trial["trial_number"]}: Objective = {trial["objective"]:.4f}')
        vars_str = ', '.join([f'{k}={v:.2f}' for k, v in trial['design_variables'].items()])
        report.append(f'   {vars_str}')
    report.append('')

    # Success assessment (if target provided)
    if target_value is not None:
        report.append('SUCCESS ASSESSMENT')
        report.append('-' * 80)
        best_objective = min(objectives)
        error = abs(best_objective - target_value)

        if error <= tolerance:
            report.append(f'[SUCCESS] Target {target_value} achieved within tolerance {tolerance}!')
            report.append(f'  Best objective: {best_objective:.4f}')
            report.append(f'  Error: {error:.4f}')
        else:
            report.append(f'[INCOMPLETE] Target {target_value} not achieved')
            report.append(f'  Best objective: {best_objective:.4f}')
            report.append(f'  Error: {error:.4f}')
            report.append(f'  Need {error - tolerance:.4f} improvement')
        report.append('')

    report.append('=' * 80)

    return '\n'.join(report)


def main():
    """Command-line interface for report generation."""
    if len(sys.argv) < 2:
        print("Usage: python generate_report.py <history_file> [target_value] [tolerance]")
        print("Example: python generate_report.py studies/my_study/2_substudies/results/optimization_history_incremental.json 115.0 0.1")
        sys.exit(1)

    history_file = Path(sys.argv[1])
    if not history_file.exists():
        print(f"Error: History file not found: {history_file}")
        sys.exit(1)

    target_value = float(sys.argv[2]) if len(sys.argv) > 2 else None
    tolerance = float(sys.argv[3]) if len(sys.argv) > 3 else 0.1

    # Generate report
    report = generate_optimization_report(history_file, target_value, tolerance)

    # Save report
    report_file = history_file.parent / 'OPTIMIZATION_REPORT.txt'
    with open(report_file, 'w') as f:
        f.write(report)

    # Print to console
    print(report)
    print()
    print(f"Report saved to: {report_file}")


if __name__ == '__main__':
    main()