diff --git a/extreme_estimator/extreme_models/utils.py b/extreme_estimator/extreme_models/utils.py
index 581723adcd6fb88b28be4863bc834550c35f1522..8ca53f60e25c8d1de8a0e614370c5ba1fff55abb 100644
--- a/extreme_estimator/extreme_models/utils.py
+++ b/extreme_estimator/extreme_models/utils.py
@@ -27,6 +27,11 @@ r.library('ismev')
# the best solution for debugging is to copy/paste the code module into a file that belongs to me, and then
# I can put print & stop in the code, and I can understand where are the problems
+def set_seed_for_test(seed=42):
+ set_seed_r(seed=seed)
+ random.seed(seed)
+
+
def set_seed_r(seed=42):
r("set.seed({})".format(seed))
@@ -42,7 +47,8 @@ class WarningMaximumAbsoluteValueTooHigh(Warning):
pass
-def safe_run_r_estimator(function, data=None, use_start=False, threshold_max_abs_value=100, **parameters) -> robjects.ListVector:
+def safe_run_r_estimator(function, data=None, use_start=False, threshold_max_abs_value=100,
+ **parameters) -> robjects.ListVector:
# Some checks for Spatial Extremes
if data is not None:
# Raise warning if the maximum absolute value is above a threshold
diff --git a/spatio_temporal_dataset/coordinates/abstract_coordinates.py b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
index cff19947b093996c7d413f33f4307a5eee35f024..5ced1941e489870e84b4a19b7a4978b51ea8b173 100644
--- a/spatio_temporal_dataset/coordinates/abstract_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
@@ -274,3 +274,6 @@ class AbstractCoordinates(object):
def __eq__(self, other):
return self.df_merged.equals(other.df_merged)
+
+ def __str__(self):
+ return self.df_all_coordinates.__str__()
diff --git a/spatio_temporal_dataset/dataset/abstract_dataset.py b/spatio_temporal_dataset/dataset/abstract_dataset.py
index d18ed288969e58b53351ea5445e6d8906332e1c7..7370746e604e441dbad855812f0fbd885327e07b 100644
--- a/spatio_temporal_dataset/dataset/abstract_dataset.py
+++ b/spatio_temporal_dataset/dataset/abstract_dataset.py
@@ -102,6 +102,11 @@ class AbstractDataset(object):
column_idxs = [idx for idx in range(self.observations.nb_obs) if idx % nb_subsets == subset_id]
self.subset_id_to_column_idxs[subset_id] = column_idxs
+ # Special methods
+
+ def __str__(self) -> str:
+ return 'coordinates: {}\nobservations: {}'.format(self.coordinates.__str__(), self.observations.__str__())
+
def get_subset_dataset(dataset: AbstractDataset, subset_id) -> AbstractDataset:
columns_idxs = dataset.subset_id_to_column_idxs[subset_id]
diff --git a/spatio_temporal_dataset/spatio_temporal_observations/abstract_spatio_temporal_observations.py b/spatio_temporal_dataset/spatio_temporal_observations/abstract_spatio_temporal_observations.py
index 483db946b86dbcd2778f4d4644120c826a0ab496..9ac704c843df470d27e23b04ff22aadf85659333 100644
--- a/spatio_temporal_dataset/spatio_temporal_observations/abstract_spatio_temporal_observations.py
+++ b/spatio_temporal_dataset/spatio_temporal_observations/abstract_spatio_temporal_observations.py
@@ -95,3 +95,8 @@ class AbstractSpatioTemporalObservations(object):
slicer: AbstractSlicer = None):
df = df_sliced(self.df_maxima_frech, split, slicer)
df.loc[:] = maxima_frech_values
+
+ def __str__(self) -> str:
+ return self._df_maxima.__str__()
+
+
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 606d52b9cde1f11b190a7fbb7065bbf79fe2ce4c..236770425e7a63d95174a020d35d81de82b7bcdf 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
@@ -1,13 +1,15 @@
+import random
import unittest
import numpy as np
import pandas as pd
from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
-from extreme_estimator.extreme_models.margin_model.linear_margin_model import LinearNonStationaryLocationMarginModel
+from extreme_estimator.extreme_models.margin_model.linear_margin_model import LinearNonStationaryLocationMarginModel, \
+ LinearStationaryMarginModel
from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
NonStationaryStationModel
-from extreme_estimator.extreme_models.utils import r, set_seed_r
+from extreme_estimator.extreme_models.utils import r, set_seed_r, set_seed_for_test
from extreme_estimator.margin_fits.gev.gevmle_fit import GevMleFit
from extreme_estimator.margin_fits.gev.ismev_gev_fit import IsmevGevFit
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -23,6 +25,7 @@ from test.test_utils import load_test_spatiotemporal_coordinates, load_smooth_ma
class TestMarginTemporal(unittest.TestCase):
def setUp(self) -> None:
+ set_seed_for_test(seed=42)
self.nb_points = 2
self.nb_steps = 5
self.nb_obs = 1
@@ -31,23 +34,23 @@ class TestMarginTemporal(unittest.TestCase):
self.start_year = 2
smooth_margin_models = LinearNonStationaryLocationMarginModel(coordinates=self.coordinates,
starting_point=self.start_year)
+
self.dataset = MarginDataset.from_sampling(nb_obs=self.nb_obs,
margin_model=smooth_margin_models,
coordinates=self.coordinates)
- def test_loading_dataset(self):
- self.assertTrue(True)
-
- # def test_gev_temporal_margin_fit_stationary(self):
- # # Create estimator
- # margin_model = StationaryStationModel(self.coordinates)
- # estimator = LinearMarginEstimator(self.dataset, margin_model)
- # estimator.fit()
- # ref = {'loc': 0.0219, 'scale': 1.0347, 'shape': 0.8295}
- # for year in range(1, 3):
- # mle_params_estimated = estimator.margin_function_fitted.get_gev_params(np.array([year])).to_dict()
- # for key in ref.keys():
- # self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
+ def test_margin_fit_stationary(self):
+ # Create estimator
+ margin_model = LinearStationaryMarginModel(self.coordinates)
+ estimator = LinearMarginEstimator(self.dataset, margin_model)
+ estimator.fit()
+ ref = {'loc': 2.2985600257321295, 'scale': 8.937484202730161, 'shape': 5.744352285758161}
+ 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