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Window-length sweep

The window-length sweep is the driver behind paper Figure 4 and the HLE dimension of Figure 2.

Run

uv run srn sweep

Two-phase ordering

The sweep runs ISOLATED methods first, then GRAPHLET methods. The ordering is load-bearing — graphlet methods read the neighbour predictions dumped by the isolated phase.

Phase Methods
ISOLATED lstm, transformer × {learnable, sinusoidal, rope}
GRAPHLET graphlet, transformer_graphlet

Outputs

  • swissrivernetwork/benchmark/dump/predictions/<path_extra_keys>-evalwl{W}/ — one wt_hat_*.csv per station and method.
  • swissrivernetwork/benchmark/visualize_results/outputs/win_lens/<graph>_<method>_win_lens_resu.csv — aggregated RMSE / MAE / NSE per window_len.

Rendering Figure 4

uv run jupyter lab swissrivernetwork/benchmark/visualize_results/window_lens_resu.ipynb

The notebook reads the CSVs above and emits the final PDF under visualize_results/figures/.

Eval-window leakage

If you are reproducing numbers from a pre-4daeff3 run, discard everything at W ≠ 90 for graphlet methods and re-run — see the explainer.