TC-Fuse evaluation pipeline — computing metrics and building figures from saved predictions with `scripts/evaluation/evaluate.py` (Hydra, `conf/evaluation.yaml`). Plugin-based: each `Evaluation` (`src/tcfuse/evaluation/`) is enabled via the `conf/evaluation/` config group and writes into its own subfolder under `paths.results/<run_id>/<experiment_name>/`. The base contract imposes no data shape (flattening is plugin-dependent); the `quantitative_metrics` plugin computes RMSE/MAE/R2/MAPE per source/channel with numpy/scikit-learn, independent of torchmetrics. Use when evaluating a prediction run, adding a new evaluation plugin, or changing the results layout.
Instalación
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
TC-Fuse evaluation pipeline — computing metrics and building figures from saved predictions with `scripts/evaluation/evaluate.py` (Hydra, `conf/evaluation.yaml`). Plugin-based: each `Evaluation` (`src/tcfuse/evaluation/`) is enabled via the `conf/evaluation/` config group and writes into its own subfolder under `paths.results/<run_id>/<experiment_name>/`. The base contract imposes no data shape (flattening is plugin-dependent); the `quantitative_metrics` plugin computes RMSE/MAE/R2/MAPE per source/channel with numpy/scikit-learn, independent of torchmetrics. Use when evaluating a prediction run, adding a new evaluation plugin, or changing the results layout.
Content has moved. The full skill documentation is in .agents/evaluate.md.
Read that file for the evaluation invocation, the plugin contract (Evaluation.run(run, output_dir)), the on-disk results layout, and how to add a new evaluation plugin.
