| |
| |
| """Tests of the release selection: sizes, caps and the 5 km rule across splits.""" |
|
|
| from __future__ import annotations |
|
|
| import numpy as np |
| import pandas as pd |
| from scipy.spatial import cKDTree |
|
|
| from package import select_release |
| from sen_bridge_build import splits |
|
|
| _KM_PER_DEG = 111.195 |
| _MIN_KM = 5.0 |
|
|
|
|
| def _world(n: int, seed: int = 0) -> pd.DataFrame: |
| """Build rows scattered over a 4x4 degree area, with clusters and a split each. |
| |
| Args: |
| n: Number of rows. |
| seed: Seed of the draw. |
| |
| Returns: |
| Rows with coordinates, region, caps columns and an assigned split. |
| """ |
| rng = np.random.default_rng(seed) |
| df = pd.DataFrame({ |
| "lat": rng.uniform(0, 4, n), |
| "lon": rng.uniform(0, 4, n), |
| "region": rng.choice(list("AB"), n), |
| "water_frac_opt": np.where(rng.random(n) < 0.2, 0.95, 0.0), |
| "lulc_bare": np.where(rng.random(n) < 0.15, 0.9, 0.0), |
| }) |
| cell = 4 |
| lat_cell = np.floor(df["lat"] * cell).astype(int) |
| df["cluster_id"] = lat_cell * 100 + np.floor(df["lon"] * cell).astype(int) |
| df["stratum"] = df["region"] |
| df["split"] = splits.assign_splits( |
| df, test_rows=n // 4, val_rows=n // 8, seed=seed |
| ) |
| return df |
|
|
|
|
| def _nearest_km(a: pd.DataFrame, b: pd.DataFrame) -> float: |
| """Smallest distance between two sets of rows in km, on a flat approximation. |
| |
| Args: |
| a: Rows with ``lat`` and ``lon``. |
| b: Rows with ``lat`` and ``lon``. |
| |
| Returns: |
| The minimum pairwise distance. |
| """ |
| dist, _ = cKDTree(b[["lat", "lon"]].to_numpy()).query( |
| a[["lat", "lon"]].to_numpy() |
| ) |
| return float(np.asarray(dist).min() * _KM_PER_DEG) |
|
|
|
|
| def test_selection_has_exact_sizes_and_keeps_every_pair_of_splits_apart() -> None: |
| df = _world(6000) |
| target = {"test": 300, "val": 100, "train": 1500} |
| out, dropped = select_release(df, target, seed=0) |
| parts = {k: out[out["split"] == k] for k in target} |
| assert {k: len(v) for k, v in parts.items()} == target |
| for first, second in (("test", "val"), ("test", "train"), ("val", "train")): |
| assert _nearest_km(parts[first], parts[second]) >= _MIN_KM |
| assert set(dropped) == {"val_near_test", "train_near_test", "train_near_val"} |
|
|
|
|
| def _test_ids(df: pd.DataFrame) -> set[int]: |
| """Return the row labels that belong to the test split. |
| |
| Args: |
| df: Rows with a ``split`` column. |
| |
| Returns: |
| The labels of the test rows. |
| """ |
| return {int(i) for i in df.index[df["split"] == "test"]} |
|
|
|
|
| def test_test_set_is_independent_of_the_later_splits() -> None: |
| df = _world(6000) |
| small = select_release(df, {"test": 300, "val": 50, "train": 500}, seed=0)[0] |
| large = select_release(df, {"test": 300, "val": 200, "train": 2000}, seed=0)[0] |
| assert _test_ids(small) == _test_ids(large) |
|
|
