diff --git a/experiment/eurocode_data/main_eurocode_drawing.py b/experiment/eurocode_data/main_eurocode_drawing.py
index ad5bc14aa36738177e393a18a67f7d7ba386a954..8321738bf7beb042b0da166d3c5508ee81704b73 100644
--- a/experiment/eurocode_data/main_eurocode_drawing.py
+++ b/experiment/eurocode_data/main_eurocode_drawing.py
@@ -23,7 +23,7 @@ mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
def massif_name_to_ordered_return_level_uncertainties(model_class, last_year_for_the_data, altitudes, massif_names,
- uncertainty_methods):
+ uncertainty_methods, temporal_covariate):
# Load model name
model_name = get_model_name(model_class)
# Load altitude visualizer
@@ -42,7 +42,8 @@ def massif_name_to_ordered_return_level_uncertainties(model_class, last_year_for
print('{} processing altitude = {} '.format(model_name, altitude))
for ci_method in uncertainty_methods:
d = visualizer.massif_name_to_altitude_and_eurocode_level_uncertainty(model_class, last_year_for_the_data,
- massif_names, ci_method)
+ massif_names, ci_method,
+ temporal_covariate)
# Append the altitude one by one
for massif_name, return_level_uncertainty in d.items():
massif_name_to_ordered_eurocode_level_uncertainty[massif_name][ci_method].append(
@@ -52,6 +53,7 @@ def massif_name_to_ordered_return_level_uncertainties(model_class, last_year_for
def main_drawing():
fast_plot = [True, False][0]
+ temporal_covariate = 2017
# Select parameters
massif_names = MASSIF_NAMES_ALPS[:]
model_class_and_last_year = [
@@ -76,9 +78,9 @@ def main_drawing():
start = time.time()
model_name_to_massif_name_to_ordered_return_level.update(
massif_name_to_ordered_return_level_uncertainties(model_class, last_year_for_the_data, altitudes,
- massif_names, uncertainty_methods))
+ massif_names, uncertainty_methods, temporal_covariate))
duration = time.time() - start
- print(model_class, duration)
+ print('Duration:', model_class, duration)
# Transform the dictionary into the desired format
massif_name_to_model_name_to_ordered_return_level_uncertainties = {}
for massif_name in massif_names:
diff --git a/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/study_visualizer.py b/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/study_visualizer.py
index 35e3b9eb6f9f4f7e0857ff11acb3c3a5e93ac327..727af31eb0493010901e0cbd8c90f586c1a0953e 100644
--- a/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/study_visualizer.py
+++ b/experiment/meteo_france_data/scm_models_data/visualization/study_visualization/study_visualizer.py
@@ -354,9 +354,9 @@ class StudyVisualizer(VisualizationParameters):
start_year, stop_year = self.study.start_year_and_stop_year
return list(range(start_year, stop_year))
- def massif_name_to_altitude_and_eurocode_level_uncertainty(self, model_class, last_year_for_the_data, massif_names, ci_method) -> Dict[str, Tuple[int, EurocodeConfidenceIntervalFromExtremes]]:
+ def massif_name_to_altitude_and_eurocode_level_uncertainty(self, model_class, last_year_for_the_data, massif_names, ci_method, temporal_covariate) -> Dict[str, Tuple[int, EurocodeConfidenceIntervalFromExtremes]]:
massif_ids_and_names = [(massif_id, massif_name) for massif_id, massif_name in enumerate(self.study.study_massif_names) if massif_name in massif_names]
- arguments = [[last_year_for_the_data, self.smooth_maxima_x_y(massif_id), model_class, ci_method] for massif_id, _ in massif_ids_and_names]
+ arguments = [[last_year_for_the_data, self.smooth_maxima_x_y(massif_id), model_class, ci_method, temporal_covariate] for massif_id, _ in massif_ids_and_names]
if self.multiprocessing:
with Pool(NB_CORES) as p:
res = p.starmap(compute_eurocode_confidence_interval, arguments)
diff --git a/extreme_fit/distribution/gev/main_fevd_bayesian.R b/extreme_fit/distribution/gev/main_fevd_bayesian.R
index de7e0fdd1d6b3341314c3cd44a30e3599bdbd0e8..8cea57bbac012a40ce328cf99c909ce5b05ee6f5 100644
--- a/extreme_fit/distribution/gev/main_fevd_bayesian.R
+++ b/extreme_fit/distribution/gev/main_fevd_bayesian.R
@@ -9,9 +9,10 @@ library(SpatialExtremes)
source('fevd_fixed.R')
# Sample from a GEV
set.seed(42)
-N <- 100
-loc = 0; scale = 1; shape <- 0.1
+N <- 50
+loc = 0; scale = 1; shape <- 1
x_gev <- rgev(N, loc = loc, scale = scale, shape = shape)
+start_loc = 0; start_scale = 1; start_shape = 1
# fevdPriorMy <- function (theta, q, p, log = FALSE){
# x = theta["shape"] + 0.5
@@ -43,11 +44,25 @@ x_gev <- rgev(N, loc = loc, scale = scale, shape = shape)
# Add covariate
coord <- matrix(ncol=1, nrow = N)
-coord[,1]=seq(1,N,1)
+coord[,1]=seq(0,N-1,1)
+print(coord)
colnames(coord) = c("T")
coord = data.frame(coord, stringsAsFactors = TRUE)
-# res = fevd_fixed(x_gev, data=coord, location.fun= ~T, scale.fun= ~T, method='Bayesian', priorFun="fevdPriorCustom", priorParams=list(q=c(6), p=c(9)), iter=5000, verbose=TRUE, use.phi=FALSE)
-res = fevd_fixed(x_gev, data=coord, method='Bayesian', priorFun="fevdPriorCustom", priorParams=list(q=c(6), p=c(9)), iter=5000, verbose=TRUE, use.phi=FALSE)
+res = fevd_fixed(x_gev, data=coord, location.fun= ~T, method='Bayesian', priorFun="fevdPriorCustom", priorParams=list(q=c(6), p=c(9)), iter=5000, verbose=TRUE, use.phi=FALSE)
+# res = fevd_fixed(x_gev, data=coord, method='Bayesian', priorFun="fevdPriorCustom", priorParams=list(q=c(6), p=c(9)), iter=5000, verbose=TRUE, use.phi=FALSE)
+print(res)
+
+v = make.qcov(res, vals = list(mu1 = c(0.0)))
+# # res_find = findpars(res, FUN = "mean" , tscale = FALSE, qcov = v,
+# # burn.in=4998)
+# # print(res_find)
+# #
+res_ci = ci(res, alpha = 0.05, type = c("return.level"),
+ burn.in=499,
+ return.period = 50, FUN = "mean", tscale = FALSE, qcov=v)
+print(res_ci)
+print(res_ci[1, ])
+
# Some display for the results
# print(res)
@@ -67,9 +82,9 @@ res = fevd_fixed(x_gev, data=coord, method='Bayesian', priorFun="fevdPriorCustom
# Confidence interval
-res_ci = ci(res, alpha = 0.05, type = c("return.level"),
- return.period = 50, FUN = "mean", tscale = FALSE)
-print(res_ci)
+# res_ci = ci(res, alpha = 0.05, type = c("return.level"),
+# return.period = 50, FUN = "mean", tscale = FALSE)
+# print(res_ci)
# print(attributes(res_ci))
# print(dimnames(res_ci))
# print(res_ci[1])
@@ -77,15 +92,16 @@ print(res_ci)
# covariates = c(1.0, 100.0, 1000.0)
# v = make.qcov(res, vals = list(mu1 = covariates, sigma1 = covariates))
-# v = make.qcov(res, vals = list(mu1 = c(0.0), sigma1 = c(0.0)))
-# res_find = findpars(res, FUN = "mean" , tscale = FALSE, qcov = v,
-# burn.in=4998)
-# print(res_find)
-#
+# v = make.qcov(res, vals = list(mu1 = c(0.0)))
+# # res_find = findpars(res, FUN = "mean" , tscale = FALSE, qcov = v,
+# # burn.in=4998)
+# # print(res_find)
+# #
# res_ci = ci(res, alpha = 0.05, type = c("return.level"),
# burn.in=4998,
# return.period = 50, FUN = "mean", tscale = FALSE, qcov=v)
# print(res_ci)
+# print(res_ci[1, ])
#
# r = qgev(0.98, 0.1427229 , 1.120554, -0.1008511)
# print(r)
diff --git a/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_extract_eurocode_return_level.py b/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_extract_eurocode_return_level.py
index 06277f7bc27be6495efcd01f2eaae365ab9f1c5b..f47b10dd3e6aa0288838ffbf3254e709e75f45b2 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_extract_eurocode_return_level.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_extract_eurocode_return_level.py
@@ -16,16 +16,12 @@ from extreme_fit.model.result_from_model_fit.result_from_extremes.result_from_ml
class AbstractExtractEurocodeReturnLevel(object):
ALPHA_CONFIDENCE_INTERVAL_UNCERTAINTY = 0.05
- def __init__(self, estimator: LinearMarginEstimator,
- ci_method,
- year_of_interest: int = YEAR_OF_INTEREST_FOR_RETURN_LEVEL,
- ):
+ def __init__(self, estimator: LinearMarginEstimator, ci_method, temporal_covariate):
self.ci_method = ci_method
self.estimator = estimator
self.result_from_fit = self.estimator.result_from_model_fit
-
- self.year_of_interest = year_of_interest
-
+ self.temporal_covariate = temporal_covariate
+ # Fixed Parameters
self.eurocode_quantile = EUROCODE_QUANTILE
self.alpha_for_confidence_interval = self.ALPHA_CONFIDENCE_INTERVAL_UNCERTAINTY
@@ -41,15 +37,15 @@ class AbstractExtractEurocodeReturnLevel(object):
class ExtractEurocodeReturnLevelFromCiMethod(AbstractExtractEurocodeReturnLevel):
result_from_fit: ResultFromMleExtremes
- def __init__(self, estimator: LinearMarginEstimator, ci_method,
- year_of_interest: int = YEAR_OF_INTEREST_FOR_RETURN_LEVEL):
- super().__init__(estimator, ci_method, year_of_interest)
+ @property
+ def transformed_temporal_covariate(self):
+ return self.estimator.dataset.coordinates.transformation.transform_float(self.temporal_covariate)
@cached_property
def confidence_interval_method(self):
return self.result_from_fit.confidence_interval_method(self.eurocode_quantile,
self.alpha_for_confidence_interval,
- self.year_of_interest)
+ self.transformed_temporal_covariate)
@property
def mean_estimate(self) -> np.ndarray:
@@ -63,9 +59,8 @@ class ExtractEurocodeReturnLevelFromCiMethod(AbstractExtractEurocodeReturnLevel)
class ExtractEurocodeReturnLevelFromMyBayesianExtremes(AbstractExtractEurocodeReturnLevel):
result_from_fit: ResultFromBayesianExtremes
- def __init__(self, estimator: LinearMarginEstimator, ci_method,
- year_of_interest: int = YEAR_OF_INTEREST_FOR_RETURN_LEVEL):
- super().__init__(estimator, ci_method, year_of_interest)
+ def __init__(self, estimator: LinearMarginEstimator, ci_method, temporal_covariate):
+ super().__init__(estimator, ci_method, temporal_covariate)
assert isinstance(self.result_from_fit, ResultFromBayesianExtremes)
@property
@@ -78,24 +73,24 @@ class ExtractEurocodeReturnLevelFromMyBayesianExtremes(AbstractExtractEurocodeRe
return margin_functions
@property
- def gev_params_from_fit_for_year_of_interest(self) -> List[GevParams]:
- return [margin_function.get_gev_params(coordinate=np.array([self.year_of_interest]), is_transformed=False)
+ def gev_params_from_fit_for_temporal_covariate(self) -> List[GevParams]:
+ return [margin_function.get_gev_params(coordinate=np.array([self.temporal_covariate]), is_transformed=False)
for margin_function in self.margin_functions_from_fit]
@cached_property
- def posterior_eurocode_return_level_samples_for_year_of_interest(self) -> np.ndarray:
+ def posterior_eurocode_return_level_samples_for_temporal_covariate(self) -> np.ndarray:
return np.array(
- [p.quantile(self.eurocode_quantile) for p in self.gev_params_from_fit_for_year_of_interest])
+ [p.quantile(self.eurocode_quantile) for p in self.gev_params_from_fit_for_temporal_covariate])
@property
def mean_estimate(self) -> np.ndarray:
# Mean posterior value here
- return np.mean(self.posterior_eurocode_return_level_samples_for_year_of_interest)
+ return np.mean(self.posterior_eurocode_return_level_samples_for_temporal_covariate)
@property
def confidence_interval(self):
# Bottom and upper quantile correspond to the quantile
bottom_quantile = self.alpha_for_confidence_interval / 2
bottom_and_upper_quantile = (bottom_quantile, 1 - bottom_quantile)
- return [np.quantile(self.posterior_eurocode_return_level_samples_for_year_of_interest, q=q)
+ return [np.quantile(self.posterior_eurocode_return_level_samples_for_temporal_covariate, q=q)
for q in bottom_and_upper_quantile]
diff --git a/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_result_from_extremes.py b/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_result_from_extremes.py
index d63f82c717501180b435ff6459fdfc198b42a533..0a93a88f6c1c0cc2bdb374dceffe465c96cc7f4e 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_result_from_extremes.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_extremes/abstract_result_from_extremes.py
@@ -14,11 +14,15 @@ class AbstractResultFromExtremes(AbstractResultFromModelFit):
super().__init__(result_from_fit)
self.gev_param_name_to_dim = gev_param_name_to_dim
+ @property
+ def is_non_stationary(self):
+ return len(self.gev_param_name_to_dim) == 0
+
def load_dataframe_from_r_matrix(self, name):
r_matrix = self.name_to_value[name]
return pd.DataFrame(np.array(r_matrix), columns=r.colnames(r_matrix))
- def confidence_interval_method(self, quantile_level, alpha_interval, temporal_covariate):
+ def confidence_interval_method(self, quantile_level, alpha_interval, transformed_temporal_covariate):
return_period = round(1 / (1 - quantile_level))
common_kwargs = {
'return.period': return_period,
@@ -27,7 +31,7 @@ class AbstractResultFromExtremes(AbstractResultFromModelFit):
'type': r.c("return.level")
}
if self.gev_param_name_to_dim:
- d = {GevParams.greek_letter_from_gev_param_name(gev_param_name) + '1': r.c(temporal_covariate) for
+ d = {GevParams.greek_letter_from_gev_param_name(gev_param_name) + '1': r.c(transformed_temporal_covariate) for
gev_param_name in self.gev_param_name_to_dim.keys()}
kwargs = {
"vals": r.list(**d
diff --git a/extreme_fit/model/result_from_model_fit/result_from_extremes/eurocode_return_level_uncertainties.py b/extreme_fit/model/result_from_model_fit/result_from_extremes/eurocode_return_level_uncertainties.py
index 8ed06a9f96e91d0f08ee80106acf162195d0fd8d..5868612141c44c6c7fe5426698db1104cdc3ff42 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_extremes/eurocode_return_level_uncertainties.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_extremes/eurocode_return_level_uncertainties.py
@@ -11,15 +11,14 @@ from extreme_fit.model.result_from_model_fit.result_from_extremes.confidence_int
ConfidenceIntervalMethodFromExtremes
-def compute_eurocode_confidence_interval(last_year_for_the_data, smooth_maxima_x_y, model_class, ci_method):
+def compute_eurocode_confidence_interval(last_year_for_the_data, smooth_maxima_x_y, model_class, ci_method, temporal_covariate):
years, smooth_maxima = smooth_maxima_x_y
idx = years.index(last_year_for_the_data) + 1
years, smooth_maxima = years[:idx], smooth_maxima[:idx]
- return EurocodeConfidenceIntervalFromExtremes.from_maxima_years_model_class(smooth_maxima, years, model_class, ci_method)
+ return EurocodeConfidenceIntervalFromExtremes.from_maxima_years_model_class(smooth_maxima, years, model_class, temporal_covariate, ci_method)
class EurocodeConfidenceIntervalFromExtremes(object):
- YEAR_OF_INTEREST = 2017
def __init__(self, mean_estimate, confidence_interval):
self.mean_estimate = mean_estimate
@@ -31,15 +30,17 @@ class EurocodeConfidenceIntervalFromExtremes(object):
@classmethod
def from_estimator_extremes(cls, estimator_extremes: LinearMarginEstimator,
- ci_method: ConfidenceIntervalMethodFromExtremes):
+ ci_method: ConfidenceIntervalMethodFromExtremes,
+ temporal_covariate: int):
if ci_method == ConfidenceIntervalMethodFromExtremes.my_bayes:
- extractor = ExtractEurocodeReturnLevelFromMyBayesianExtremes(estimator_extremes, ci_method, cls.YEAR_OF_INTEREST)
+ extractor = ExtractEurocodeReturnLevelFromMyBayesianExtremes(estimator_extremes, ci_method, temporal_covariate)
else:
- extractor = ExtractEurocodeReturnLevelFromCiMethod(estimator_extremes, ci_method, cls.YEAR_OF_INTEREST)
+ extractor = ExtractEurocodeReturnLevelFromCiMethod(estimator_extremes, ci_method, temporal_covariate)
return cls(extractor.mean_estimate, extractor.confidence_interval)
@classmethod
def from_maxima_years_model_class(cls, maxima, years, model_class,
+ temporal_covariate,
ci_method=ConfidenceIntervalMethodFromExtremes.ci_bayes):
# Load coordinates and dataset
coordinates, dataset = load_temporal_coordinates_and_dataset(maxima, years)
@@ -53,7 +54,7 @@ class EurocodeConfidenceIntervalFromExtremes(object):
fitted_estimator = fitted_linear_margin_estimator(model_class, coordinates, dataset, starting_year=1958,
fit_method=fit_method)
# Load object from result from extremes
- return cls.from_estimator_extremes(fitted_estimator, ci_method)
+ return cls.from_estimator_extremes(fitted_estimator, ci_method, temporal_covariate)
diff --git a/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_bayesian_extremes.py b/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_bayesian_extremes.py
index a760f20f44855b3895c1885f281bdae4b44f5297..851d0837e02b00ee0c0310231848269c7c8b0cdf 100644
--- a/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_bayesian_extremes.py
+++ b/extreme_fit/model/result_from_model_fit/result_from_extremes/result_from_bayesian_extremes.py
@@ -53,9 +53,13 @@ class ResultFromBayesianExtremes(AbstractResultFromExtremes):
'FUN': "mean",
}
res = r.ci(self.result_from_fit, **bayesian_ci_parameters, **common_kwargs)
- d = self.get_python_dictionary(res)
- keys = ['Posterior Mean 50-year level', '95% lower CI', '95% upper CI']
- mean_estimate, lower, upper = [np.array(d[k])[0] for k in keys]
+ if self.gev_param_name_to_dim:
+ a = np.array(res)[0]
+ lower, mean_estimate, upper = a
+ else:
+ d = self.get_python_dictionary(res)
+ keys = ['Posterior Mean 50-year level', '95% lower CI', '95% upper CI']
+ mean_estimate, lower, upper = [np.array(d[k])[0] for k in keys]
return mean_estimate, (lower, upper)
diff --git a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py
index a328257b66ee0f31b30652ce7c1612a51df51af6..98141e13346cd8bd2d5241f7bfd5994f4a73ae53 100644
--- a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py
+++ b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformation/abstract_transformation.py
@@ -13,6 +13,9 @@ class AbstractTransformation(object):
def nb_dimensions(self):
return self.df_coordinates.shape[1]
+ def transform_float(self, coordinate: float):
+ return self.transform_array(np.array([coordinate]))[0]
+
def transform_array(self, coordinate: np.ndarray):
assert len(coordinate) == self.nb_dimensions, "coordinate={}, nb_dimensions={}".format(coordinate,
self.nb_dimensions)
diff --git a/test/test_extreme_fit/test_model/test_confidence_interval.py b/test/test_extreme_fit/test_model/test_confidence_interval.py
index 79fefd84f28e34c3f8422da5fc8ac064eb80d8b4..b7b4c52849f759a6a80554ae6449336aa2ee0249 100644
--- a/test/test_extreme_fit/test_model/test_confidence_interval.py
+++ b/test/test_extreme_fit/test_model/test_confidence_interval.py
@@ -16,6 +16,8 @@ from extreme_fit.model.utils import r, set_seed_r, set_seed_for_test
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
AbstractTemporalCoordinates
+from spatio_temporal_dataset.coordinates.transformed_coordinates.transformation.abstract_transformation import \
+ CenteredScaledNormalization
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_temporal_observations import \
AbstractSpatioTemporalObservations
@@ -23,7 +25,7 @@ from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_tempor
class TestConfidenceInterval(unittest.TestCase):
- def setUp(self) -> None:
+ def load_data(self) -> None:
set_seed_for_test()
r("""
N <- 50
@@ -34,42 +36,69 @@ class TestConfidenceInterval(unittest.TestCase):
# Compute the stationary temporal margin with isMev
self.start_year = 0
df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: range(self.start_year, self.start_year + 50)})
- self.coordinates = AbstractTemporalCoordinates.from_df(df)
+ self.coordinates = self.load_coordinates(df)
df2 = pd.DataFrame(data=np.array(r['x_gev']), index=df.index)
observations = AbstractSpatioTemporalObservations(df_maxima_gev=df2)
self.dataset = AbstractDataset(observations=observations, coordinates=self.coordinates)
self.model_classes = [StationaryTemporalModel]
+ @staticmethod
+ def load_coordinates(df):
+ return AbstractTemporalCoordinates.from_df(df)
+
def compute_eurocode_ci(self, model_class):
+ self.load_data()
estimator = fitted_linear_margin_estimator(model_class, self.coordinates, self.dataset,
starting_year=0,
fit_method=self.fit_method)
- return EurocodeConfidenceIntervalFromExtremes.from_estimator_extremes(estimator, self.ci_method)
+ return EurocodeConfidenceIntervalFromExtremes.from_estimator_extremes(estimator, self.ci_method,
+ self.start_year)
+
+ @property
+ def bayesian_ci(self):
+ return {
+ StationaryTemporalModel: (6.756903450587758, 10.316338515219085, 15.77861914935531),
+ NonStationaryLocationTemporalModel: (6.588570126641043, 10.055847177064836, 14.332882862817332),
+ NonStationaryLocationAndScaleTemporalModel: (7.836837972383451, 11.162663922795906, 16.171701445841183),
+ }
def test_my_bayes(self):
self.fit_method = TemporalMarginFitMethod.extremes_fevd_bayesian
self.ci_method = ConfidenceIntervalMethodFromExtremes.my_bayes
- self.model_class_to_triplet = {
- StationaryTemporalModel: (6.756903450587758, 10.316338515219085, 15.77861914935531),
- NonStationaryLocationTemporalModel: (6.047033481540427, 9.708540966532225, 14.74058551926604),
- NonStationaryLocationAndScaleTemporalModel: (6.383536224810785, 9.69120774797993, 13.917914357321615),
- }
+ self.model_class_to_triplet = self.bayesian_ci
def test_ci_bayes(self):
self.fit_method = TemporalMarginFitMethod.extremes_fevd_bayesian
self.ci_method = ConfidenceIntervalMethodFromExtremes.ci_bayes
- self.model_class_to_triplet = {
- StationaryTemporalModel: (6.756903450587758, 10.316338515219085, 15.77861914935531),
- # NonStationaryLocationTemporalModel: (6.047033481540427, 9.708540966532225, 14.74058551926604),
- # NonStationaryLocationAndScaleTemporalModel: (6.383536224810785, 9.69120774797993, 13.917914357321615),
- }
+ self.model_class_to_triplet = self.bayesian_ci
def tearDown(self) -> None:
for model_class, expected_triplet in self.model_class_to_triplet.items():
- eurocode_ci = self.compute_eurocode_ci(StationaryTemporalModel)
+ eurocode_ci = self.compute_eurocode_ci(model_class)
found_triplet = eurocode_ci.triplet
for a, b in zip(expected_triplet, found_triplet):
- self.assertAlmostEqual(a, b, msg="{} {}".format(model_class, found_triplet))
+ self.assertAlmostEqual(a, b, msg="{} \n{}".format(model_class, found_triplet))
+
+
+class TestConfidenceIntervalModifiedCoordinates(TestConfidenceInterval):
+
+ @staticmethod
+ def load_coordinates(df):
+ return AbstractTemporalCoordinates.from_df(df, transformation_class=CenteredScaledNormalization)
+
+ @property
+ def bayesian_ci(self):
+ return {
+ StationaryTemporalModel: (6.756903450587758, 10.316338515219085, 15.77861914935531),
+ NonStationaryLocationTemporalModel: (6.266027110993808, 10.063368195790687, 14.894103640762097),
+ NonStationaryLocationAndScaleTemporalModel: (5.554116722722492, 13.714431163455535, 26.929963957448642),
+ }
+
+ def test_my_bayes(self):
+ super().test_my_bayes()
+
+ def test_ci_bayes(self):
+ super().test_ci_bayes()
if __name__ == '__main__':