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19_research.py
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65 lines (51 loc) · 2.09 KB
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#!/usr/bin/env python3
"""
Step 19: Research Processing (Thin Orchestrator)
This step orchestrates research processing for GNN models.
Architectural Role:
This is a "thin orchestrator" - a minimal script that delegates core functionality
to the corresponding module (src/research/). It handles argument parsing, logging
setup, and calls the actual processing functions from the research module.
Pipeline Flow:
main.py → 19_research.py (this script) → research/ (modular implementation)
How to run:
python src/19_research.py --target-dir input/gnn_files --output-dir output --verbose
python src/main.py # (runs as part of the pipeline)
Expected outputs:
- Research processing results in the specified output directory
- Comprehensive research reports and summaries
- Actionable error messages if dependencies or paths are missing
- Clear logging of all resolved arguments and paths
If you encounter errors:
- Check that research dependencies are installed
- Check that src/research/ contains research modules
- Check that the output directory is writable
- Verify research configuration and requirements
"""
import sys
from pathlib import Path
# Add src to path for imports
sys.path.insert(0, str(Path(__file__).parent))
from utils.pipeline_template import create_standardized_pipeline_script
# Import module function
try:
from research import process_research
except ImportError:
import logging
from typing import Any, Optional
def process_research(target_dir: Path, output_dir: Path, logger: Optional[logging.Logger] = None, **kwargs: Any) -> bool:
"""Recovery research processing when module unavailable."""
if logger is None:
logger = logging.getLogger(__name__)
logger.warning("Research module not available - using recovery")
return True
run_script = create_standardized_pipeline_script(
"19_research.py",
process_research,
"Research processing for GNN models",
)
def main() -> int:
"""Main entry point for the research step."""
return run_script()
if __name__ == "__main__":
raise SystemExit(main())