- generate the scenario data
- evaluate the solutions to generate the training data for the approximation network
The evaluation of the solutions is done by the evaluate.py
and data.py
script. The script is based on https://github.com/GOCompetition/Evaluation.git
- src
- generate_data.py \\generate the scenario data
- random_load_profile.py \\generate the random load profile
- generate_data_parallel.py and scenario_operations.py \\used to generate the scenario data in parallel
- get_stage2_pen.py \\gather the second stage penalty from each detail file, and splitting the data for training the approximation neural network.
- generate_result.py \\generate solving time and solution results into the the ./result folder
- model
folder to store the trained model weights
- result \\store the result csv files
- result.ipynb and time_result.ipynb \\generate the result table and plots
- load_model.py \\load the trained model and write the weights of the neural network in to txt files in the model folder
- test.py, test.sh \\the function to call the evalutation.run method to evaluate the solutions
- examples
- case2 # an example datafile
python generate_data.py
python random_load_profile.py
bash test.sh
python get_stage2_pen.py
python approx_model.py