Systematic Analysis of Instances with TIMES

This repository contains a Python script (run.py) designed to perform a systematic analysis of a set of instances using the TIMES source code. The script processes instance data, generates benchmark statistics, and compares results against a ground truth from a previous run.

Table of Contents

• Installation
• Usage
• File Structure
• Configuration
• Output
• Contributing

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/yourrepository.git
   cd yourrepository

2. Ensure you have Python installed (Python 3.6 or higher).

3. Install necessary dependencies:

   pip install -r requirements.txt

Usage

1. Choose a Solve Mode: Choose your solvemode from

   1. SOLVE:
      • Runs and solves all instances and generates a savepoint file TIMES_p.gdx in the output directory.
      • The savepoint file contains the information on the current solution point can be used to skip the solve of a future run to save time.
   2. LOADSOLUTION:
      • Requires a savepoint file (TIMES_p.gdx) in the output directory.
      • Loads information from a previous solve instead of resolving the model.

2. Prepare Instance Data: Place your instance data into subfolders within the data directory. Each subfolder should contain the relevant data for an instance.

   Add all instances you want to analyze to the instances list where each tuple contains (<folder_name>,<run_file>):

   instances = [
       ("Instance_1", "Instance_1.RUN"),
       ("Instance_2", "Instance_2.RUN"),
   ]

3. Specify TIMES Source Code Location: In the run.py script, set the path to the TIMES source code. Modify the following line:

   TIMES_SOURCE_PATH = '/path/to/times/source'

4. Run the Script: Execute the script to start the analysis:

   python run.py

File Structure

• run.py: The main script for performing the analysis.
• summarize_results.py: Script for analyzing output files and writing statistics.
• data/: Directory where instance data subfolders are placed.
• output/: Directory where log and profile files are written.
• ground_truth_result_overview.csv: CSV file containing results from a previous run for comparison.
• results_overview.csv: CSV file containing results from the latest run of run.py.
• results.md: Markdown file generated with benchmark statistics and analysis. Compares ground_truth_result_overview.csv and results_overview.csv.

Configuration

Ensure the following configurations are set in run.py before running the script:

• TIMES_SOURCE_PATH: Path to the TIMES source code.
• Data directory structure should be maintained as specified.
• instances: List of tuples containing instances subject to analyses.
• solvemode: Mode of solving (solve vs skip solve)

Output

The script generates several outputs:

• .log, .lst and Profile Files: Written to subfolders within the output directory.
• Comparison to Ground Truth: A markdown file (results.md) with statistics on all analyzed results.
• Benchmark Results: A CSV file (results_overview.csv) containing statistics from the latest run.
• Profile Summary: A markdown file (profile_summary.md) with detailed run times for GAMS assignments or equations.

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add your feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a Pull Request.

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Feel free to reach out if you have any questions or need further assistance!

Happy analyzing!