`swissrivernetwork.benchmark.dataset`¶

swissrivernetwork.benchmark.dataset ¶

Dataset primitives for the Swiss River Network Benchmark.

This module exposes:

:func:read_graph / :func:read_csv_train / :func:read_csv_test — low-level graph + CSV readers for the three datasets (swiss-1990, swiss-2010, zurich).
:func:select_isolated_station — pure column-select used by isolated LSTM / Transformer training; does not drop rows.
:class:SequenceDataset — sliding-window dataset over per-station time series. short_subsequence_method="drop" silently drops runs shorter than window_len (this is why isolated wt_hat dumps at large eval window lengths can cover fewer days than the raw test CSV — a fact that motivates the inner-join in :func:util.merge_graphlet_dfs).
:class:SequenceWindowedDataset — graphlet-style dataset that stacks neighbor features alongside the target station.

The module is import-cheap (no torch CUDA calls at import time) so it can be imported by CPU-only tooling such as the CLI or the documentation builder.

SequenceWindowedDataset ¶

Bases: SequenceDataset

SequenceFullDataset ¶

Bases: SequenceDataset

Returns the full available sequence (no windowing)

read_stations ¶

read_stations(graph_name, base_dir: str | Path = PROJ_DIR)

read_graph ¶

read_graph(graph_name, base_dir: str | Path = PROJ_DIR)

read_csv_train ¶

read_csv_train(graph_name, base_dir: str | Path = PROJ_DIR)

read_csv_test ¶

read_csv_test(graph_name, base_dir: str | Path = PROJ_DIR)

read_csv_prediction_train ¶

read_csv_prediction_train(
    graph_name: str,
    method: str,
    station,
    predict_dump_dir: str | Path | None = None,
    base_dir: str | Path = PROJ_DIR,
)

read_csv_prediction_test ¶

read_csv_prediction_test(
    graph_name: str,
    method: str,
    station,
    predict_dump_dir: str | Path | None = None,
    based_dir: str | Path = PROJ_DIR,
)

swissrivernetwork.benchmark.dataset¶