Anto01
d228ccec66
Moved experimental LLM integration code to optimization_engine/future/: - llm_optimization_runner.py - Runtime LLM API runner - llm_workflow_analyzer.py - Workflow analysis - inline_code_generator.py - Auto-generate calculations - hook_generator.py - Auto-generate hooks - report_generator.py - LLM report generation - extractor_orchestrator.py - Extractor orchestration Added comprehensive optimization_engine/future/README.md explaining: - MVP LLM strategy (Claude Code skills, not runtime LLM) - Why files were archived - When to revisit post-MVP - Production architecture reference Production runner confirmed: optimization_engine/runner.py is sole active runner. This establishes clear separation between: - Production code (stable, no runtime LLM dependencies) - Experimental code (archived for post-MVP exploration) Part of Phase 1: Core Stabilization & Organization for MVP Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
135 lines
5.0 KiB
Python
135 lines
5.0 KiB
Python
"""
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Report Generator Utility
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Generates Markdown/HTML/PDF reports for optimization studies
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"""
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import json
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from pathlib import Path
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from typing import Optional
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import markdown
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from datetime import datetime
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def generate_study_report(
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study_dir: Path,
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output_format: str = "markdown",
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include_llm_summary: bool = False
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) -> Optional[Path]:
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"""
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Generate a report for the study.
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Args:
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study_dir: Path to the study directory
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output_format: 'markdown', 'html', or 'pdf'
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include_llm_summary: Whether to include AI-generated summary
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Returns:
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Path to the generated report file
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"""
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try:
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# Load data
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config_path = study_dir / "1_setup" / "optimization_config.json"
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history_path = study_dir / "2_results" / "optimization_history_incremental.json"
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if not config_path.exists() or not history_path.exists():
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return None
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with open(config_path) as f:
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config = json.load(f)
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with open(history_path) as f:
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history = json.load(f)
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# Find best trial
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best_trial = None
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if history:
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best_trial = min(history, key=lambda x: x['objective'])
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# Generate Markdown content
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md_content = f"""# Optimization Report: {config.get('study_name', study_dir.name)}
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**Date**: {datetime.now().strftime('%Y-%m-%d %H:%M')}
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**Status**: {'Completed' if len(history) >= config.get('optimization_settings', {}).get('n_trials', 50) else 'In Progress'}
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## Executive Summary
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{_generate_summary(history, best_trial, include_llm_summary)}
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## Study Configuration
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- **Objectives**: {', '.join([o['name'] for o in config.get('objectives', [])])}
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- **Design Variables**: {len(config.get('design_variables', []))} variables
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- **Total Trials**: {len(history)}
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## Best Result (Trial #{best_trial['trial_number'] if best_trial else 'N/A'})
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- **Objective Value**: {best_trial['objective'] if best_trial else 'N/A'}
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- **Parameters**:
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"""
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if best_trial:
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for k, v in best_trial['design_variables'].items():
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md_content += f" - **{k}**: {v:.4f}\n"
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md_content += "\n## Optimization Progress\n"
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md_content += "The optimization process showed convergence towards the optimal solution.\n"
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# Save report based on format
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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output_dir = study_dir / "2_results"
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if output_format in ['markdown', 'md']:
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output_path = output_dir / f"optimization_report_{timestamp}.md"
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with open(output_path, 'w') as f:
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f.write(md_content)
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elif output_format == 'html':
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output_path = output_dir / f"optimization_report_{timestamp}.html"
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html_content = markdown.markdown(md_content)
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# Add basic styling
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styled_html = f"""
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<html>
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<head>
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<style>
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body {{ font-family: sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }}
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h1 {{ color: #2563eb; }}
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h2 {{ border-bottom: 1px solid #e5e7eb; padding-bottom: 10px; margin-top: 30px; }}
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code {{ background: #f3f4f6; padding: 2px 4px; rounded: 4px; }}
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</style>
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</head>
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<body>
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{html_content}
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</body>
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</html>
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"""
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with open(output_path, 'w') as f:
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f.write(styled_html)
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elif output_format == 'pdf':
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# Requires weasyprint
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try:
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from weasyprint import HTML
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output_path = output_dir / f"optimization_report_{timestamp}.pdf"
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html_content = markdown.markdown(md_content)
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HTML(string=html_content).write_pdf(str(output_path))
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except ImportError:
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print("WeasyPrint not installed, falling back to HTML")
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return generate_study_report(study_dir, 'html', include_llm_summary)
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return output_path
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except Exception as e:
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print(f"Report generation error: {e}")
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return None
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def _generate_summary(history, best_trial, use_llm):
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if use_llm:
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return "[AI Summary Placeholder] The optimization successfully identified a design that minimizes mass while satisfying all constraints."
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if not history:
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return "No trials completed yet."
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improvement = 0
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if len(history) > 1:
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first = history[0]['objective']
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best = best_trial['objective']
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improvement = ((first - best) / first) * 100
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return f"The optimization run completed {len(history)} trials. The best design found (Trial #{best_trial['trial_number']}) achieved an objective value of {best_trial['objective']:.4f}, representing a {improvement:.1f}% improvement over the initial design."
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