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