diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/non_stationary_trends.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/non_stationary_trends.py
index 33b0031b6f2f86f5237d00c8d8c5d35a083b4063..ea6eff9af8698895c7ecc74731bad8c59bf93940 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/non_stationary_trends.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/non_stationary_trends.py
@@ -18,8 +18,9 @@ from utils import get_display_name_from_object_type
class AbstractNonStationaryTrendTest(object):
RESULT_ATTRIBUTE_METRIC = 'deviance'
- def __init__(self, dataset: AbstractDataset, estimator_class,
+ def __init__(self, dataset: AbstractDataset, verbose, estimator_class,
stationary_margin_model_class, non_stationary_margin_model_class):
+ self.verbose = verbose
self.dataset = dataset
self.estimator_class = estimator_class
self.stationary_margin_model_class = stationary_margin_model_class
@@ -33,9 +34,10 @@ class AbstractNonStationaryTrendTest(object):
if (margin_model_class, starting_point) not in self._margin_model_class_and_starting_point_to_estimator:
margin_model = margin_model_class(coordinates=self.dataset.coordinates, starting_point=starting_point)
estimator = self._load_estimator(margin_model)
- estimator_name = get_display_name_from_object_type(estimator)
- margin_model_name = get_display_name_from_object_type(margin_model)
- print('Fitting {} with margin: {} for starting_point={}'.format(estimator_name, margin_model_name, starting_point))
+ if self.verbose:
+ estimator_name = get_display_name_from_object_type(estimator)
+ margin_model_name = get_display_name_from_object_type(margin_model)
+ print('Fitting {} with margin: {} for starting_point={}'.format(estimator_name, margin_model_name, starting_point))
estimator.fit()
self._margin_model_class_and_starting_point_to_estimator[(margin_model_class, starting_point)] = estimator
return self._margin_model_class_and_starting_point_to_estimator[(margin_model_class, starting_point)]
@@ -79,12 +81,15 @@ class AbstractNonStationaryTrendTest(object):
mu1_trends = [self.get_mu1(starting_point=year) for year in years]
ax2 = ax.twinx()
color_mu1 = 'c'
- print(mu1_trends)
+
+ if self.verbose:
+ print(mu1_trends)
ax2.plot(years, mu1_trends, color_mu1 + 'o-')
ax2.set_ylabel('mu1 parameter', color=color_mu1)
ax.set_xlabel('starting year for the linear trend of mu1')
- # align_yaxis_on_zero(ax, ax2)
+ if min(mu1_trends) < 0.0 < max(mu1_trends):
+ align_yaxis_on_zero(ax, ax2)
title = self.display_name
ax.set_title(title)
ax.legend()
@@ -94,7 +99,7 @@ class AbstractNonStationaryTrendTest(object):
if complete_analysis:
year_min, year_max, step = 1960, 1990, 1
else:
- year_min, year_max, step = 1960, 1990, 10
+ year_min, year_max, step = 1960, 1990, 5
years = list(range(year_min, year_max + 1, step))
return years
@@ -111,8 +116,9 @@ class IndependenceLocationTrendTest(AbstractNonStationaryTrendTest):
class ConditionalIndedendenceLocationTrendTest(AbstractNonStationaryTrendTest):
- def __init__(self, dataset):
+ def __init__(self, dataset, verbose=False):
super().__init__(dataset=dataset,
+ verbose=verbose,
estimator_class=LinearMarginEstimator,
stationary_margin_model_class=LinearStationaryMarginModel,
non_stationary_margin_model_class=LinearNonStationaryLocationMarginModel)
@@ -124,8 +130,9 @@ class ConditionalIndedendenceLocationTrendTest(AbstractNonStationaryTrendTest):
class MaxStableLocationTrendTest(AbstractNonStationaryTrendTest):
- def __init__(self, dataset, max_stable_model):
+ def __init__(self, dataset, max_stable_model, verbose=False):
super().__init__(dataset=dataset,
+ verbose=verbose,
estimator_class=FullEstimatorInASingleStepWithSmoothMargin,
stationary_margin_model_class=LinearStationaryMarginModel,
non_stationary_margin_model_class=LinearNonStationaryLocationMarginModel)
diff --git a/extreme_estimator/extreme_models/result_from_fit.py b/extreme_estimator/extreme_models/result_from_fit.py
index 669c2f87ce35c32f43a5c60b832a46ddbe3ab861..e8f50a720b5034076227f764426a1fdb65d96180 100644
--- a/extreme_estimator/extreme_models/result_from_fit.py
+++ b/extreme_estimator/extreme_models/result_from_fit.py
@@ -37,6 +37,10 @@ class ResultFromFit(object):
def deviance(self):
raise NotImplementedError
+ @property
+ def convergence(self) -> str:
+ raise NotImplementedError
+
class ResultFromIsmev(ResultFromFit):
diff --git a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/uniform_normalization.py b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/uniform_normalization.py
index 950679bd927f721de7ad0b92a62e292e7bc8be98..8922ff7715b88eacc2ea8426d22728e18090ab2e 100644
--- a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/uniform_normalization.py
+++ b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/uniform_normalization.py
@@ -31,6 +31,31 @@ class BetweenZeroAndOneNormalization(UniformNormalization):
return coord_scaled
+class BetweenZeroAndTenNormalization(BetweenZeroAndOneNormalization):
+
+ def uniform_normalization(self, coordinate_value: np.ndarray) -> np.ndarray:
+ return super().uniform_normalization(coordinate_value) / 10
+
+
+epsilon = 0.001
+
+
+class BetweenZeroAndOneNormalizationMinEpsilon(BetweenZeroAndOneNormalization):
+
+ def __init__(self, df_coordinates):
+ super().__init__(df_coordinates)
+ gap = self.max_coord - self.min_coord
+ self.min_coord -= gap * epsilon
+
+
+class BetweenZeroAndOneNormalizationMaxEpsilon(BetweenZeroAndOneNormalization):
+
+ def __init__(self, df_coordinates):
+ super().__init__(df_coordinates)
+ gap = self.max_coord - self.min_coord
+ self.max_coord += gap * epsilon
+
+
class BetweenMinusOneAndOneNormalization(BetweenZeroAndOneNormalization):
"""Normalize such that min(coord) >= (-1,-1) and max(coord) <= (1,1)"""
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 9ac704c843df470d27e23b04ff22aadf85659333..e65c488e289f21786eb1a3b1ab6db475abfbb35e 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
@@ -38,6 +38,12 @@ class AbstractSpatioTemporalObservations(object):
else:
return self.df_maxima_gev
+ def normalize(self):
+ # It should stay superior to 0 and lower or equal to 1
+ # Thus the easiest way to do that is to divide by the maximum
+ maxima = self._df_maxima.values.flatten().max()
+ self._df_maxima /= maxima
+
@property
def df_maxima_merged(self) -> pd.DataFrame:
df_maxima_list = []
@@ -99,4 +105,8 @@ class AbstractSpatioTemporalObservations(object):
def __str__(self) -> str:
return self._df_maxima.__str__()
+ @_df_maxima.setter
+ def _df_maxima(self, value):
+ self.__df_maxima = value
+
diff --git a/test/test_experiment/test_coordinate_sensitivity.py b/test/test_experiment/test_coordinate_sensitivity.py
index 8fcc5363587b856eaebe18ad32e2bdba0ef83f60..2672d7a6768e68ec9b52f1afca76e4bf5f780cf1 100644
--- a/test/test_experiment/test_coordinate_sensitivity.py
+++ b/test/test_experiment/test_coordinate_sensitivity.py
@@ -7,7 +7,8 @@ from experiment.meteo_france_SCM_study.visualization.study_visualization.non_sta
ConditionalIndedendenceLocationTrendTest
from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.uniform_normalization import \
- BetweenZeroAndOneNormalization, BetweenMinusOneAndOneNormalization
+ BetweenZeroAndOneNormalization, BetweenMinusOneAndOneNormalization, BetweenZeroAndTenNormalization, \
+ BetweenZeroAndOneNormalizationMinEpsilon, BetweenZeroAndOneNormalizationMaxEpsilon
from utils import get_display_name_from_object_type
@@ -15,11 +16,14 @@ class TestCoordinateSensitivity(unittest.TestCase):
DISPLAY = False
def test_coordinate_normalization_sensitivity(self):
- altitudes = [3000]
- transformation_classes = [BetweenZeroAndOneNormalization, BetweenMinusOneAndOneNormalization][:]
- for transformation_class in transformation_classes:
- study_classes = [CrocusSwe]
- for study in study_iterator_global(study_classes, altitudes=altitudes, verbose=False):
+ altitudes = [300, 600, 900, 1200, 2100, 3000][-1:]
+ transformation_classes = [None, BetweenZeroAndOneNormalization, BetweenZeroAndOneNormalizationMinEpsilon, BetweenZeroAndOneNormalizationMaxEpsilon][1:2]
+
+ study_classes = [CrocusSwe]
+ for study in study_iterator_global(study_classes, altitudes=altitudes, verbose=False):
+ if self.DISPLAY:
+ print(study.altitude)
+ for transformation_class in transformation_classes:
study_visualizer = StudyVisualizer(study, transformation_class=transformation_class)
study_visualizer.temporal_non_stationarity = True
trend_test = ConditionalIndedendenceLocationTrendTest(study_visualizer.dataset)
@@ -27,10 +31,20 @@ class TestCoordinateSensitivity(unittest.TestCase):
mu1s = [trend_test.get_mu1(year) for year in years]
if self.DISPLAY:
print('Stationary')
- print(trend_test.get_estimator(trend_test.stationary_margin_model_class, starting_point=None).margin_function_fitted.coef_dict)
+ stationary_est = trend_test.get_estimator(trend_test.stationary_margin_model_class,
+ starting_point=None)
+ print(stationary_est.result_from_fit.convergence)
+ print(stationary_est.margin_function_fitted.coef_dict)
print('Non Stationary')
- print(trend_test.get_estimator(trend_test.non_stationary_margin_model_class, starting_point=1960).margin_function_fitted.coef_dict)
+ non_stationary_est = trend_test.get_estimator(trend_test.non_stationary_margin_model_class,
+ starting_point=1960)
+ print(non_stationary_est.result_from_fit.convergence)
+ non_stationary_est = trend_test.get_estimator(trend_test.non_stationary_margin_model_class,
+ starting_point=1990)
+ print(non_stationary_est.result_from_fit.convergence)
+ print(non_stationary_est.margin_function_fitted.coef_dict)
print(get_display_name_from_object_type(transformation_class), 'mu1s: ', mu1s)
+ print('\n')
self.assertTrue(0.0 not in mu1s)