diff --git a/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py b/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py
index 236770425e7a63d95174a020d35d81de82b7bcdf..4107e4b9234de11fcdeb4262e32aee34e4f450b1 100644
--- a/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py
+++ b/test/test_extreme_estimator/test_extreme_models/test_margin_temporal.py
@@ -27,16 +27,14 @@ class TestMarginTemporal(unittest.TestCase):
def setUp(self) -> None:
set_seed_for_test(seed=42)
self.nb_points = 2
- self.nb_steps = 5
+ self.nb_steps = 50
self.nb_obs = 1
# Load some 2D spatial coordinates
self.coordinates = load_test_spatiotemporal_coordinates(nb_steps=self.nb_steps, nb_points=self.nb_points)[1]
- self.start_year = 2
- smooth_margin_models = LinearNonStationaryLocationMarginModel(coordinates=self.coordinates,
- starting_point=self.start_year)
-
+ smooth_margin_model = LinearNonStationaryLocationMarginModel(coordinates=self.coordinates,
+ starting_point=2)
self.dataset = MarginDataset.from_sampling(nb_obs=self.nb_obs,
- margin_model=smooth_margin_models,
+ margin_model=smooth_margin_model,
coordinates=self.coordinates)
def test_margin_fit_stationary(self):
@@ -44,52 +42,56 @@ class TestMarginTemporal(unittest.TestCase):
margin_model = LinearStationaryMarginModel(self.coordinates)
estimator = LinearMarginEstimator(self.dataset, margin_model)
estimator.fit()
- ref = {'loc': 2.2985600257321295, 'scale': 8.937484202730161, 'shape': 5.744352285758161}
+ ref = {'loc': 1.3403346591679877, 'scale': 1.054867229157924, 'shape': 0.713700960727747}
for year in range(1, 3):
coordinate = np.array([0.0, 0.0, year])
mle_params_estimated = estimator.margin_function_fitted.get_gev_params(coordinate).to_dict()
- print(mle_params_estimated)
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
- #
- # def test_gev_temporal_margin_fit_nonstationary(self):
- # # Create estimator
- # margin_model = NonStationaryStationModel(self.coordinates)
- # estimator = LinearMarginEstimator(self.dataset, margin_model)
- # estimator.fit()
- # self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
- # # Checks that parameters returned are indeed different
- # mle_params_estimated_year1 = estimator.margin_function_fitted.get_gev_params(np.array([1])).to_dict()
- # mle_params_estimated_year3 = estimator.margin_function_fitted.get_gev_params(np.array([3])).to_dict()
- # self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
- #
- # def test_gev_temporal_margin_fit_nonstationary_with_start_point(self):
- # # Create estimator
- # estimator = self.fit_non_stationary_estimator(starting_point=3)
- # self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
- # # Checks starting point parameter are well passed
- # self.assertEqual(3, estimator.margin_function_fitted.starting_point)
- # # Checks that parameters returned are indeed different
- # mle_params_estimated_year1 = estimator.margin_function_fitted.get_gev_params(np.array([1])).to_dict()
- # mle_params_estimated_year3 = estimator.margin_function_fitted.get_gev_params(np.array([3])).to_dict()
- # self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year3)
- # mle_params_estimated_year5 = estimator.margin_function_fitted.get_gev_params(np.array([5])).to_dict()
- # self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year3)
- #
- # def fit_non_stationary_estimator(self, starting_point):
- # margin_model = NonStationaryStationModel(self.coordinates, starting_point=starting_point + self.start_year)
- # estimator = LinearMarginEstimator(self.dataset, margin_model)
- # estimator.fit()
- # return estimator
- #
- # def test_two_different_starting_points(self):
- # # Create two different estimators
- # estimator1 = self.fit_non_stationary_estimator(starting_point=3)
- # estimator2 = self.fit_non_stationary_estimator(starting_point=28)
- # mu1_estimator1 = estimator1.margin_function_fitted.mu1_temporal_trend
- # mu1_estimator2 = estimator2.margin_function_fitted.mu1_temporal_trend
- # print(mu1_estimator1, mu1_estimator2)
- # self.assertNotEqual(mu1_estimator1, mu1_estimator2)
+
+ def test_margin_fit_nonstationary(self):
+ # Create estimator
+ margin_model = LinearNonStationaryLocationMarginModel(self.coordinates)
+ estimator = LinearMarginEstimator(self.dataset, margin_model)
+ estimator.fit()
+ self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
+ # Checks that parameters returned are indeed different
+ coordinate1 = np.array([0.0, 0.0, 1])
+ mle_params_estimated_year1 = estimator.margin_function_fitted.get_gev_params(coordinate1).to_dict()
+ coordinate3 = np.array([0.0, 0.0, 3])
+ mle_params_estimated_year3 = estimator.margin_function_fitted.get_gev_params(coordinate3).to_dict()
+ self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
+
+ def test_margin_fit_nonstationary_with_start_point(self):
+ # Create estimator
+ estimator = self.fit_non_stationary_estimator(starting_point=2)
+ print(estimator.margin_function_fitted.mu1_temporal_trend)
+ self.assertNotEqual(estimator.margin_function_fitted.mu1_temporal_trend, 0.0)
+ # Checks starting point parameter are well passed
+ self.assertEqual(2, estimator.margin_function_fitted.starting_point)
+ # Checks that parameters returned are indeed different
+ coordinate1 = np.array([0.0, 0.0, 1])
+ mle_params_estimated_year1 = estimator.margin_function_fitted.get_gev_params(coordinate1).to_dict()
+ coordinate2 = np.array([0.0, 0.0, 2])
+ mle_params_estimated_year2 = estimator.margin_function_fitted.get_gev_params(coordinate2).to_dict()
+ self.assertEqual(mle_params_estimated_year1, mle_params_estimated_year2)
+ coordinate5 = np.array([0.0, 0.0, 5])
+ mle_params_estimated_year5 = estimator.margin_function_fitted.get_gev_params(coordinate5).to_dict()
+ self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year2)
+
+ def fit_non_stationary_estimator(self, starting_point):
+ margin_model = LinearNonStationaryLocationMarginModel(self.coordinates, starting_point=starting_point)
+ estimator = LinearMarginEstimator(self.dataset, margin_model)
+ estimator.fit()
+ return estimator
+
+ def test_two_different_starting_points(self):
+ # Create two different estimators
+ estimator1 = self.fit_non_stationary_estimator(starting_point=3)
+ estimator2 = self.fit_non_stationary_estimator(starting_point=20)
+ mu1_estimator1 = estimator1.margin_function_fitted.mu1_temporal_trend
+ mu1_estimator2 = estimator2.margin_function_fitted.mu1_temporal_trend
+ self.assertNotEqual(mu1_estimator1, mu1_estimator2)
if __name__ == '__main__':