How to Use The Tool

1. Initialize a workspace (example name: my_project). By default, only the sample dataset (ds0) is included. You can choose to download all datasets if needed:

   On macOS, use python3 -c if python -c is not available.

   python -c "import pynnlf; pynnlf.init('my_project')"

2. Fill in the input values for your forecast problem and model specification in specs/experiment.yaml. Example:

   dataset: ds0 forecast_horizon: fh1 # fh1 = 30 minutes ahead model: m6 # Linear regression hyperparameter: hp1 # Hyperparameter ID

3. Run the experiment:

   python -c "import pynnlf; pynnlf.run_experiment('example_project/specs/experiment.yaml')"

4. Run a batch of experiments (optional):

   python -c "import pynnlf; pynnlf.run_experiment_batch('example_project/specs/batch.yaml')"

5. Recap multiple experiments into one CSV (optional):

   python -c "import pynnlf; pynnlf.recap_experiments('example_project/experiment_result')"

   This writes example_project/experiment_result/a1_experiment_result.csv and skips missing or malformed CSVs with warnings.

6. The tool outputs evaluation results to the experiment_result/ folder inside your workspace.

For the list of available datasets & models, how to modify model hyperparameter, how to add a model, how to add a dataset, and exhaustive list of API Reference see the Detailed Guide page.