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Preserve class labels in ClassificationKriging predictions #317

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2 changes: 1 addition & 1 deletion src/pykrige/ck.py
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Original file line number Diff line number Diff line change
Expand Up @@ -167,7 +167,7 @@ def predict(self, p, x, **kwargs):
pred_proba_ilr = self.krige_residual(x, **kwargs) + ml_pred_ilr
pred_proba = inverse_ilr_transformation(pred_proba_ilr)

return np.argmax(pred_proba, axis=1)
return self.classes_[np.argmax(pred_proba, axis=1)]

def krige_residual(self, x, **kwargs):
"""
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31 changes: 31 additions & 0 deletions tests/test_classification_krige.py
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Expand Up @@ -62,6 +62,37 @@ def test_classification_krige():
assert class_model.score(X_test, lon_lat_test, y_test) > 0.25


@pytest.mark.skipif(not SKLEARN_INSTALLED, reason="requires scikit-learn")
@pytest.mark.parametrize("method", ["ordinary", "universal"])
@pytest.mark.parametrize(
"classes",
[
np.array([1, 3, 7]),
np.array([-4, -2, 5]),
np.array(["clay", "peat", "sand"]),
np.arange(3),
],
)
def test_classification_krige_prediction_labels(method, classes):
rng = np.random.RandomState(4)
p = rng.uniform(size=(12, 2))
x = rng.uniform(size=(12, 2))
y = classes[np.arange(12) % 3]
model = ClassificationKriging(
classification_model=RandomForestClassifier(n_estimators=8, random_state=0),
method=method,
n_closest_points=4,
variogram_parameters=[1.0, 0.0],
)
model.fit(p, x, y[:, None])

prediction = model.predict(p, x)
np.testing.assert_array_equal(prediction, y)
assert prediction.shape == y.shape
assert prediction.dtype == y.dtype
assert model.score(p, x, y) == 1.0


@pytest.mark.skipif(not SKLEARN_INSTALLED, reason="requires scikit-learn")
def test_krige_classification_housing():
import ssl
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