| import pytest |
| import numpy as np |
| from time import time |
| from polire import ( |
| IDW, |
| Spline, |
| Trend, |
| |
| Kriging, |
| NaturalNeighbor, |
| SpatialAverage, |
| CustomInterpolator, |
| |
| ) |
| from sklearn.linear_model import LinearRegression |
|
|
| X = np.random.rand(20, 2) |
| y = np.random.rand(20) |
|
|
| X_new = np.random.rand(40, 2) |
|
|
|
|
| @pytest.mark.parametrize( |
| "model", |
| [ |
| IDW(), |
| Spline(), |
| Trend(), |
| |
| Kriging(), |
| NaturalNeighbor(), |
| SpatialAverage(), |
| CustomInterpolator(LinearRegression()), |
| |
| ], |
| ) |
| def test_fit_predict(model): |
| init = time() |
| model.fit(X, y) |
| y_new = model.predict(X_new) |
|
|
| assert y_new.shape == (40,) |
| print("Passed", "Time:", np.round(time() - init, 3), "seconds") |
|
|
|
|
| @pytest.mark.skip(reason="Temporarily disabled") |
| def test_nsgp(): |
| model = NSGP() |
| init = time() |
| model.fit(X, y, **{"ECM": X @ X.T}) |
| y_new = model.predict(X_new) |
|
|
| assert y_new.shape == (40,) |
| assert y_new.sum() == y_new.sum() |
| print("Passed", "Time:", np.round(time() - init, 3), "seconds") |
|
|