From a594c050bed7807285401ef2b8b27e6a4e376ac1 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Wed, 18 Mar 2020 15:43:35 +0100
Subject: [PATCH] [refactoring] abstract estimator return an abstract function
---
.../study_visualization/study_visualizer.py | 2 +-
.../regression_margin/regression_margin.py | 2 +-
experiment/simulation/abstract_simulation.py | 2 +-
.../trend_analysis/non_stationary_trends.py | 2 +-
.../abstract_gev_trend_test.py | 22 ++++++-------
extreme_fit/estimator/abstract_estimator.py | 3 +-
.../full_estimator/abstract_full_estimator.py | 4 +--
.../abstract_margin_estimator.py | 2 +-
.../abstract_quantile_estimator.py | 4 +--
.../abstract_quantile_function.py | 3 +-
.../abstract_margin_function.py | 3 +-
.../margin_model/abstract_margin_model.py | 1 +
.../test_coordinate_sensitivity.py | 6 ++--
.../test_gev_temporal_extremes_bayesian.py | 10 +++---
.../test_gev_temporal_extremes_gumbel.py | 2 +-
.../test_gev_temporal_extremes_mle.py | 10 +++---
.../test_gev/test_gev_temporal_is_mev.py | 20 ++++++------
.../test_estimator/test_margin_estimators.py | 2 +-
.../test_estimator/test_quantile_estimator.py | 2 +-
.../test_model/test_margin_temporal.py | 24 +++++++-------
.../test_margin_temporal_transformed.py | 32 +++++++++----------
.../test_model/test_max_stable_temporal.py | 24 +++++++-------
22 files changed, 93 insertions(+), 89 deletions(-)
rename extreme_fit/{estimator/quantile_estimator => function}/abstract_quantile_function.py (71%)
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 264c7566..9d34c7c1 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
@@ -768,7 +768,7 @@ class StudyVisualizer(VisualizationParameters):
estimator.fit()
# Set visualization attributes for margin_fct
- margin_fct = estimator.margin_function_from_fit
+ margin_fct = estimator.function_from_fit
margin_fct._visualization_x_limits = self.study.visualization_x_limits
margin_fct._visualization_y_limits = self.study.visualization_y_limits
margin_fct.mask_2D = self.study.mask_french_alps
diff --git a/experiment/regression_margin/regression_margin.py b/experiment/regression_margin/regression_margin.py
index 0dfcdccc..33b67924 100644
--- a/experiment/regression_margin/regression_margin.py
+++ b/experiment/regression_margin/regression_margin.py
@@ -57,7 +57,7 @@ for i in range(nb_estimator):
margin_model_for_estimator = margin_model_for_estimator_class(coordinates)
full_estimator = FullEstimatorInASingleStepWithSmoothMargin(dataset, margin_model_for_estimator, max_stable_model)
full_estimator.fit()
- full_estimator.margin_function_from_fit.visualize_function(axes=axes, show=False)
+ full_estimator.function_from_fit.visualize_function(axes=axes, show=False)
plt.show()
# Display all the margin on the same graph for comparison
diff --git a/experiment/simulation/abstract_simulation.py b/experiment/simulation/abstract_simulation.py
index 2a94a6bc..70f59120 100644
--- a/experiment/simulation/abstract_simulation.py
+++ b/experiment/simulation/abstract_simulation.py
@@ -59,7 +59,7 @@ class AbstractSimulation(object):
estimator = estimator_class.from_dataset(dataset) # type: AbstractEstimator
# Fit the estimator and get the margin_function
estimator.fit()
- margin_function_fitted_list.append(estimator.margin_function_from_fit)
+ margin_function_fitted_list.append(estimator.function_from_fit)
# Individual error dict
self.dump_fitted_margins_pickle(estimator_class, margin_function_fitted_list)
diff --git a/experiment/trend_analysis/non_stationary_trends.py b/experiment/trend_analysis/non_stationary_trends.py
index 2c63e957..cf1a8c8e 100644
--- a/experiment/trend_analysis/non_stationary_trends.py
+++ b/experiment/trend_analysis/non_stationary_trends.py
@@ -75,7 +75,7 @@ class AbstractNonStationaryTrendTest(object):
def get_mu_coefs(self, starting_point):
# for the non stationary model gives the mu1 parameters that was fitted
estimator = self.get_estimator(starting_point)
- margin_function = estimator.margin_function_from_fit # type: LinearMarginFunction
+ margin_function = estimator.function_from_fit # type: LinearMarginFunction
assert isinstance(margin_function, LinearMarginFunction)
mu_coefs = [margin_function.mu_intercept, margin_function.mu1_temporal_trend]
if self.has_spatial_coordinates:
diff --git a/experiment/trend_analysis/univariate_test/extreme_trend_test/abstract_gev_trend_test.py b/experiment/trend_analysis/univariate_test/extreme_trend_test/abstract_gev_trend_test.py
index 2782f461..45470bae 100644
--- a/experiment/trend_analysis/univariate_test/extreme_trend_test/abstract_gev_trend_test.py
+++ b/experiment/trend_analysis/univariate_test/extreme_trend_test/abstract_gev_trend_test.py
@@ -131,20 +131,20 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
return np.sign(self.time_derivative_of_return_level)
def get_non_stationary_linear_coef(self, gev_param_name: str):
- return self.unconstrained_estimator.margin_function_from_fit.get_coef(gev_param_name,
- AbstractCoordinates.COORDINATE_T)
+ return self.unconstrained_estimator.function_from_fit.get_coef(gev_param_name,
+ AbstractCoordinates.COORDINATE_T)
@cached_property
def unconstrained_estimator_gev_params(self) -> GevParams:
# Constant parameters correspond to the gev params in 1958
- return self.unconstrained_estimator.margin_function_from_fit.get_gev_params(coordinate=np.array([1958]),
- is_transformed=False)
+ return self.unconstrained_estimator.function_from_fit.get_gev_params(coordinate=np.array([1958]),
+ is_transformed=False)
@cached_property
def constrained_estimator_gev_params(self) -> GevParams:
# Constant parameters correspond to any gev params
- return self.constrained_estimator.margin_function_from_fit.get_gev_params(coordinate=np.array([1958]),
- is_transformed=False)
+ return self.constrained_estimator.function_from_fit.get_gev_params(coordinate=np.array([1958]),
+ is_transformed=False)
def time_derivative_times_years(self, nb_years):
# Compute the slope strength
@@ -163,7 +163,7 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
def relative_change_in_return_level(self, initial_year, final_year):
return_level_values = []
for year in [initial_year, final_year]:
- gev_params = self.unconstrained_estimator.margin_function_from_fit.get_gev_params(
+ gev_params = self.unconstrained_estimator.function_from_fit.get_gev_params(
coordinate=np.array([year]),
is_transformed=False)
return_level_values.append(gev_params.quantile(self.quantile_level))
@@ -289,7 +289,7 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
label = 'Y({})'.format(year) if year is not None else label
if year is None:
year = 2019
- gev_params_year = self.unconstrained_estimator.margin_function_from_fit.get_gev_params(
+ gev_params_year = self.unconstrained_estimator.function_from_fit.get_gev_params(
coordinate=np.array([year]),
is_transformed=False)
extended_maxima = [gev_params_year.gumbel_inverse_standardization(q) for q in extended_quantiles]
@@ -380,7 +380,7 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
def compute_empirical_quantiles(self, estimator):
empirical_quantiles = []
for year, maximum in sorted(zip(self.years, self.maxima), key=lambda t: t[1]):
- gev_param = estimator.margin_function_from_fit.get_gev_params(
+ gev_param = estimator.function_from_fit.get_gev_params(
coordinate=np.array([year]),
is_transformed=False)
maximum_standardized = gev_param.gumbel_standardization(maximum)
@@ -426,8 +426,8 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
plt.gca().set_ylim(bottom=0)
def get_gev_params_with_big_shape_and_correct_shape(self):
- gev_params = self.unconstrained_estimator.margin_function_from_fit.get_gev_params(coordinate=np.array([YEAR_OF_INTEREST_FOR_RETURN_LEVEL]),
- is_transformed=False) # type: GevParams
+ gev_params = self.unconstrained_estimator.function_from_fit.get_gev_params(coordinate=np.array([YEAR_OF_INTEREST_FOR_RETURN_LEVEL]),
+ is_transformed=False) # type: GevParams
gev_params_with_corrected_shape = GevParams(loc=gev_params.location,
scale=gev_params.scale,
shape=0.5)
diff --git a/extreme_fit/estimator/abstract_estimator.py b/extreme_fit/estimator/abstract_estimator.py
index d9be7274..c8e93cad 100644
--- a/extreme_fit/estimator/abstract_estimator.py
+++ b/extreme_fit/estimator/abstract_estimator.py
@@ -2,6 +2,7 @@ from typing import Union
from cached_property import cached_property
+from extreme_fit.function.abstract_function import AbstractFunction
from extreme_fit.model.result_from_model_fit.abstract_result_from_model_fit import AbstractResultFromModelFit
from extreme_fit.function.margin_function.abstract_margin_function import \
AbstractMarginFunction
@@ -35,7 +36,7 @@ class AbstractEstimator(object):
return self._result_from_fit
@cached_property
- def margin_function_from_fit(self) -> AbstractMarginFunction:
+ def function_from_fit(self) -> AbstractFunction:
raise NotImplementedError
# Short cut properties
diff --git a/extreme_fit/estimator/full_estimator/abstract_full_estimator.py b/extreme_fit/estimator/full_estimator/abstract_full_estimator.py
index 2657a881..b6ff59ce 100644
--- a/extreme_fit/estimator/full_estimator/abstract_full_estimator.py
+++ b/extreme_fit/estimator/full_estimator/abstract_full_estimator.py
@@ -34,7 +34,7 @@ class SmoothMarginalsThenUnitaryMsp(AbstractFullEstimator):
maxima_frech = AbstractMarginModel.gev2frech(maxima_gev=maxima_gev_train,
coordinates_values=self.dataset.coordinates_values(
self.train_split),
- margin_function=self.margin_estimator.margin_function_from_fit)
+ margin_function=self.margin_estimator.function_from_fit)
# Update maxima frech field through the dataset object
self.dataset.set_maxima_frech(maxima_frech, split=self.train_split)
# Estimate the max stable parameters
@@ -86,7 +86,7 @@ class FullEstimatorInASingleStepWithSmoothMargin(AbstractFullEstimator):
)
@cached_property
- def margin_function_from_fit(self) -> LinearMarginFunction:
+ def function_from_fit(self) -> LinearMarginFunction:
return load_margin_function(self, self.linear_margin_model)
diff --git a/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py b/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
index 0e6f6537..117594e3 100644
--- a/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
+++ b/extreme_fit/estimator/margin_estimator/abstract_margin_estimator.py
@@ -47,5 +47,5 @@ class LinearMarginEstimator(AbstractMarginEstimator):
return compute_nllh(self, self.maxima_gev_train, self.coordinate_temp, self.margin_model)
@cached_property
- def margin_function_from_fit(self) -> LinearMarginFunction:
+ def function_from_fit(self) -> LinearMarginFunction:
return load_margin_function(self, self.margin_model)
diff --git a/extreme_fit/estimator/quantile_estimator/abstract_quantile_estimator.py b/extreme_fit/estimator/quantile_estimator/abstract_quantile_estimator.py
index 739c8929..d43aae92 100644
--- a/extreme_fit/estimator/quantile_estimator/abstract_quantile_estimator.py
+++ b/extreme_fit/estimator/quantile_estimator/abstract_quantile_estimator.py
@@ -2,7 +2,7 @@ from cached_property import cached_property
from extreme_fit.estimator.abstract_estimator import AbstractEstimator
from extreme_fit.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
-from extreme_fit.estimator.quantile_estimator.abstract_quantile_function import AbstractQuantileFunction
+from extreme_fit.function.abstract_quantile_function import AbstractQuantileFunction
from extreme_fit.model.margin_model.linear_margin_model.linear_margin_model import LinearMarginModel
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
@@ -26,5 +26,5 @@ class QuantileEstimatorFromMargin(AbstractQuantileEstimator, LinearMarginEstimat
@cached_property
def quantile_function_from_fit(self) -> AbstractQuantileFunction:
- linear_margin_function = super().margin_function_from_fit
+ linear_margin_function = super().function_from_fit
return AbstractQuantileFunction(linear_margin_function, self.quantile)
diff --git a/extreme_fit/estimator/quantile_estimator/abstract_quantile_function.py b/extreme_fit/function/abstract_quantile_function.py
similarity index 71%
rename from extreme_fit/estimator/quantile_estimator/abstract_quantile_function.py
rename to extreme_fit/function/abstract_quantile_function.py
index ef9af2fa..4c06c95e 100644
--- a/extreme_fit/estimator/quantile_estimator/abstract_quantile_function.py
+++ b/extreme_fit/function/abstract_quantile_function.py
@@ -1,9 +1,10 @@
import numpy as np
+from extreme_fit.function.abstract_function import AbstractFunction
from extreme_fit.function.margin_function.abstract_margin_function import AbstractMarginFunction
-class AbstractQuantileFunction(object):
+class AbstractQuantileFunction(AbstractFunction):
def __init__(self, margin_function: AbstractMarginFunction, quantile: float):
self.margin_function = margin_function
diff --git a/extreme_fit/function/margin_function/abstract_margin_function.py b/extreme_fit/function/margin_function/abstract_margin_function.py
index fb10aa47..994e7ebc 100644
--- a/extreme_fit/function/margin_function/abstract_margin_function.py
+++ b/extreme_fit/function/margin_function/abstract_margin_function.py
@@ -7,12 +7,13 @@ import pandas as pd
from experiment.meteo_france_data.scm_models_data.visualization.utils import create_adjusted_axes
from extreme_fit.distribution.gev.gev_params import GevParams
from experiment.meteo_france_data.plot.create_shifted_cmap import imshow_shifted
+from extreme_fit.function.abstract_function import AbstractFunction
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
from spatio_temporal_dataset.slicer.split import Split
from root_utils import cached_property
-class AbstractMarginFunction(object):
+class AbstractMarginFunction(AbstractFunction):
"""
AbstractMarginFunction maps points from a space S (could be 1D, 2D,...) to R^3 (the 3 parameters of the GEV)
"""
diff --git a/extreme_fit/model/margin_model/abstract_margin_model.py b/extreme_fit/model/margin_model/abstract_margin_model.py
index 24593139..ef7ae1a7 100644
--- a/extreme_fit/model/margin_model/abstract_margin_model.py
+++ b/extreme_fit/model/margin_model/abstract_margin_model.py
@@ -32,6 +32,7 @@ class AbstractMarginModel(AbstractModel, ABC):
raise NotImplementedError
def default_load_margin_functions(self, margin_function_class):
+ # todo: check it i could remove these attributes
self.margin_function_sample = margin_function_class(coordinates=self.coordinates,
default_params=GevParams.from_dict(self.params_sample))
self.margin_function_start_fit = margin_function_class(coordinates=self.coordinates,
diff --git a/test/test_experiment/test_coordinate_sensitivity.py b/test/test_experiment/test_coordinate_sensitivity.py
index e709029a..f0eb3a4b 100644
--- a/test/test_experiment/test_coordinate_sensitivity.py
+++ b/test/test_experiment/test_coordinate_sensitivity.py
@@ -33,15 +33,15 @@ class TestCoordinateSensitivity(unittest.TestCase):
if self.DISPLAY:
print('Stationary')
stationary_est = trend_test.get_estimator(starting_point=None)
- print(stationary_est.margin_function_from_fit.coordinates.df_all_coordinates)
+ print(stationary_est.function_from_fit.coordinates.df_all_coordinates)
print(stationary_est.result_from_model_fit.convergence)
- print(stationary_est.margin_function_from_fit.coef_dict)
+ print(stationary_est.function_from_fit.coef_dict)
print('Non Stationary')
non_stationary_est = trend_test.get_estimator(starting_point=1960)
print(non_stationary_est.result_from_model_fit.convergence)
non_stationary_est = trend_test.get_estimator(starting_point=1990)
print(non_stationary_est.result_from_model_fit.convergence)
- print(non_stationary_est.margin_function_from_fit.coef_dict)
+ print(non_stationary_est.function_from_fit.coef_dict)
print(get_display_name_from_object_type(transformation_class), 'mu1s: ', mu1s)
print('\n')
self.assertTrue(0.0 not in mu1s)
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_bayesian.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_bayesian.py
index ec0b6977..d3e3a9e4 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_bayesian.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_bayesian.py
@@ -43,7 +43,7 @@ class TestGevTemporalExtremesBayesian(unittest.TestCase):
fit_method=self.fit_method)
ref = {'loc': 0.34272436381693616, 'scale': 1.3222588712831973, 'shape': 0.30491484962825105}
for year in range(1, 3):
- mle_params_estimated = estimator.margin_function_from_fit.get_gev_params(np.array([year])).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
@@ -55,8 +55,8 @@ class TestGevTemporalExtremesBayesian(unittest.TestCase):
mu1_values = estimator.result_from_model_fit.df_posterior_samples.iloc[:, 1]
self.assertTrue((mu1_values != 0).any())
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.margin_function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.margin_function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
def test_gev_temporal_margin_fit_non_stationary_location_and_scale(self):
@@ -67,8 +67,8 @@ class TestGevTemporalExtremesBayesian(unittest.TestCase):
mu1_values = estimator.result_from_model_fit.df_posterior_samples.iloc[:, 1]
self.assertTrue((mu1_values != 0).any())
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.margin_function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.margin_function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_gumbel.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_gumbel.py
index 8aee8f21..018aeebb 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_gumbel.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_gumbel.py
@@ -42,7 +42,7 @@ class TestGevTemporalExtremesGumbel(unittest.TestCase):
fit_method=TemporalMarginFitMethod.extremes_fevd_mle)
ref = {'loc': -0.0862185692806497, 'scale': 1.0818465357627252, 'shape': 0}
for year in range(1, 3):
- mle_params_estimated = estimator.margin_function_from_fit.get_gev_params(np.array([year])).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_mle.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_mle.py
index 94f98974..88677c67 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_mle.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_extremes_mle.py
@@ -43,7 +43,7 @@ class TestGevTemporalExtremesMle(unittest.TestCase):
fit_method=self.fit_method)
ref = {'loc': 0.02191974259369493, 'scale': 1.0347946062900268, 'shape': 0.829052520147379}
for year in range(1, 3):
- mle_params_estimated = estimator.margin_function_from_fit.get_gev_params(np.array([year])).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
self.assertAlmostEqual(estimator.result_from_model_fit.nllh, estimator.nllh)
@@ -54,8 +54,8 @@ class TestGevTemporalExtremesMle(unittest.TestCase):
starting_year=0,
fit_method=self.fit_method)
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.margin_function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.margin_function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
self.assertAlmostEqual(estimator.result_from_model_fit.nllh, estimator.nllh)
@@ -66,8 +66,8 @@ class TestGevTemporalExtremesMle(unittest.TestCase):
starting_year=0,
fit_method=self.fit_method)
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.margin_function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.margin_function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(np.array([3])).to_dict()
self.assertNotEqual(mle_params_estimated_year1, mle_params_estimated_year3)
self.assertAlmostEqual(estimator.result_from_model_fit.nllh, estimator.nllh)
diff --git a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_is_mev.py b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_is_mev.py
index 2e52087c..333cfd19 100644
--- a/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_is_mev.py
+++ b/test/test_extreme_fit/test_distribution/test_gev/test_gev_temporal_is_mev.py
@@ -45,7 +45,7 @@ class TestGevTemporal(unittest.TestCase):
fit_method=self.fit_method)
ref = {'loc': 0.04309190816463247, 'scale': 2.0688696961628437, 'shape': 0.8291528207825063}
for year in range(1, 3):
- mle_params_estimated = estimator.margin_function_from_fit.get_gev_params(np.array([year])).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_gev_params(np.array([year])).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
@@ -56,21 +56,21 @@ class TestGevTemporal(unittest.TestCase):
estimator = LinearMarginEstimator(self.dataset, margin_model)
estimator.fit()
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.margin_function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.margin_function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.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_from_fit.mu1_temporal_trend, 0.0)
+ self.assertNotEqual(estimator.function_from_fit.mu1_temporal_trend, 0.0)
# Checks starting point parameter are well passed
- self.assertEqual(3, estimator.margin_function_from_fit.starting_point)
+ self.assertEqual(3, estimator.function_from_fit.starting_point)
# Checks that parameters returned are indeed different
- mle_params_estimated_year1 = estimator.margin_function_from_fit.get_gev_params(np.array([1])).to_dict()
- mle_params_estimated_year3 = estimator.margin_function_from_fit.get_gev_params(np.array([3])).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(np.array([1])).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.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_from_fit.get_gev_params(np.array([5])).to_dict()
+ mle_params_estimated_year5 = estimator.function_from_fit.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):
@@ -84,8 +84,8 @@ class TestGevTemporal(unittest.TestCase):
# 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_from_fit.mu1_temporal_trend
- mu1_estimator2 = estimator2.margin_function_from_fit.mu1_temporal_trend
+ mu1_estimator1 = estimator1.function_from_fit.mu1_temporal_trend
+ mu1_estimator2 = estimator2.function_from_fit.mu1_temporal_trend
self.assertNotEqual(mu1_estimator1, mu1_estimator2)
diff --git a/test/test_extreme_fit/test_estimator/test_margin_estimators.py b/test/test_extreme_fit/test_estimator/test_margin_estimators.py
index 7f5eabb2..9a5a5c55 100644
--- a/test/test_extreme_fit/test_estimator/test_margin_estimators.py
+++ b/test/test_extreme_fit/test_estimator/test_margin_estimators.py
@@ -34,7 +34,7 @@ class TestSmoothMarginEstimator(unittest.TestCase):
# Plot
if self.DISPLAY:
margin_model.margin_function_sample.visualize_function(show=True)
- estimator.margin_function_from_fit.visualize_function(show=True)
+ estimator.function_from_fit.visualize_function(show=True)
self.assertTrue(True)
diff --git a/test/test_extreme_fit/test_estimator/test_quantile_estimator.py b/test/test_extreme_fit/test_estimator/test_quantile_estimator.py
index 9bf1a3c1..bd517938 100644
--- a/test/test_extreme_fit/test_estimator/test_quantile_estimator.py
+++ b/test/test_extreme_fit/test_estimator/test_quantile_estimator.py
@@ -32,7 +32,7 @@ class TestSmoothMarginEstimator(unittest.TestCase):
for quantile_estimator in quantile_estimators:
quantile_estimator.fit()
- print(quantile_estimator.margin_function_from_fit)
+ print(quantile_estimator.function_from_fit)
# self.assertTrue(True)
diff --git a/test/test_extreme_fit/test_model/test_margin_temporal.py b/test/test_extreme_fit/test_model/test_margin_temporal.py
index fdf159f2..31b87e87 100644
--- a/test/test_extreme_fit/test_model/test_margin_temporal.py
+++ b/test/test_extreme_fit/test_model/test_margin_temporal.py
@@ -33,7 +33,7 @@ class TestMarginTemporal(unittest.TestCase):
ref = {'loc': 1.3456595684773085, 'scale': 1.090369430386199, 'shape': 0.6845422250749476}
for year in range(1, 3):
coordinate = np.array([0.0, 0.0, year])
- mle_params_estimated = estimator.margin_function_from_fit.get_gev_params(coordinate).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_gev_params(coordinate).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
@@ -42,32 +42,32 @@ class TestMarginTemporal(unittest.TestCase):
margin_model = LinearNonStationaryLocationMarginModel(self.coordinates)
estimator = LinearMarginEstimator(self.dataset, margin_model)
estimator.fit()
- self.assertNotEqual(estimator.margin_function_from_fit.mu1_temporal_trend, 0.0)
+ self.assertNotEqual(estimator.function_from_fit.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_from_fit.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1).to_dict()
coordinate3 = np.array([0.0, 0.0, 3])
- mle_params_estimated_year3 = estimator.margin_function_from_fit.get_gev_params(coordinate3).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.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)
# By default, estimator find the good margin
- self.assertNotEqual(estimator.margin_function_from_fit.mu1_temporal_trend, 0.0)
- self.assertAlmostEqual(estimator.margin_function_from_fit.mu1_temporal_trend,
+ self.assertNotEqual(estimator.function_from_fit.mu1_temporal_trend, 0.0)
+ self.assertAlmostEqual(estimator.function_from_fit.mu1_temporal_trend,
self.smooth_margin_model.margin_function_sample.mu1_temporal_trend,
places=3)
# Checks starting point parameter are well passed
- self.assertEqual(2, estimator.margin_function_from_fit.starting_point)
+ self.assertEqual(2, estimator.function_from_fit.starting_point)
# Checks that parameters returned are indeed different
coordinate1 = np.array([0.0, 0.0, 1])
- mle_params_estimated_year1 = estimator.margin_function_from_fit.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1).to_dict()
coordinate2 = np.array([0.0, 0.0, 2])
- mle_params_estimated_year2 = estimator.margin_function_from_fit.get_gev_params(coordinate2).to_dict()
+ mle_params_estimated_year2 = estimator.function_from_fit.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_from_fit.get_gev_params(coordinate5).to_dict()
+ mle_params_estimated_year5 = estimator.function_from_fit.get_gev_params(coordinate5).to_dict()
self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year2)
def fit_non_stationary_estimator(self, starting_point):
@@ -80,8 +80,8 @@ class TestMarginTemporal(unittest.TestCase):
# 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_from_fit.mu1_temporal_trend
- mu1_estimator2 = estimator2.margin_function_from_fit.mu1_temporal_trend
+ mu1_estimator1 = estimator1.function_from_fit.mu1_temporal_trend
+ mu1_estimator2 = estimator2.function_from_fit.mu1_temporal_trend
self.assertNotEqual(mu1_estimator1, mu1_estimator2)
diff --git a/test/test_extreme_fit/test_model/test_margin_temporal_transformed.py b/test/test_extreme_fit/test_model/test_margin_temporal_transformed.py
index f4a42a81..66392b46 100644
--- a/test/test_extreme_fit/test_model/test_margin_temporal_transformed.py
+++ b/test/test_extreme_fit/test_model/test_margin_temporal_transformed.py
@@ -41,8 +41,8 @@ class TestMarginTemporalTransformed(unittest.TestCase):
'shape': 0.7289248773961512}
for year in range(1, 3):
coordinate = np.array([0.0, 0.0, year])
- mle_params_estimated = estimator.margin_function_from_fit.get_gev_params(coordinate,
- is_transformed=False).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_gev_params(coordinate,
+ is_transformed=False).to_dict()
self.assertEqual(mle_params_estimated, ref)
def test_margin_fit_nonstationary(self):
@@ -50,32 +50,32 @@ class TestMarginTemporalTransformed(unittest.TestCase):
margin_model = LinearNonStationaryLocationMarginModel(self.coordinates)
estimator = LinearMarginEstimator(self.dataset, margin_model)
estimator.fit()
- self.assertNotEqual(estimator.margin_function_from_fit.mu1_temporal_trend, 0.0)
+ self.assertNotEqual(estimator.function_from_fit.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_from_fit.get_gev_params(coordinate1,
- is_transformed=False).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1,
+ is_transformed=False).to_dict()
coordinate3 = np.array([0.0, 0.0, 3])
- mle_params_estimated_year3 = estimator.margin_function_from_fit.get_gev_params(coordinate3,
- is_transformed=False).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.get_gev_params(coordinate3,
+ is_transformed=False).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)
# By default, estimator find the good margin
- self.assertNotEqual(estimator.margin_function_from_fit.mu1_temporal_trend, 0.0)
+ self.assertNotEqual(estimator.function_from_fit.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_from_fit.get_gev_params(coordinate1,
- is_transformed=False).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1,
+ is_transformed=False).to_dict()
coordinate2 = np.array([0.0, 0.0, 2])
- mle_params_estimated_year2 = estimator.margin_function_from_fit.get_gev_params(coordinate2,
- is_transformed=False).to_dict()
+ mle_params_estimated_year2 = estimator.function_from_fit.get_gev_params(coordinate2,
+ is_transformed=False).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_from_fit.get_gev_params(coordinate5,
- is_transformed=False).to_dict()
+ mle_params_estimated_year5 = estimator.function_from_fit.get_gev_params(coordinate5,
+ is_transformed=False).to_dict()
self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year2)
def fit_non_stationary_estimator(self, starting_point):
@@ -88,8 +88,8 @@ class TestMarginTemporalTransformed(unittest.TestCase):
# 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_from_fit.mu1_temporal_trend
- mu1_estimator2 = estimator2.margin_function_from_fit.mu1_temporal_trend
+ mu1_estimator1 = estimator1.function_from_fit.mu1_temporal_trend
+ mu1_estimator2 = estimator2.function_from_fit.mu1_temporal_trend
self.assertNotEqual(mu1_estimator1, mu1_estimator2)
diff --git a/test/test_extreme_fit/test_model/test_max_stable_temporal.py b/test/test_extreme_fit/test_model/test_max_stable_temporal.py
index 8a6a6b3b..cd111c34 100644
--- a/test/test_extreme_fit/test_model/test_max_stable_temporal.py
+++ b/test/test_extreme_fit/test_model/test_max_stable_temporal.py
@@ -37,7 +37,7 @@ class TestMaxStableTemporal(unittest.TestCase):
ref = {'loc': 1.2091156634312243, 'scale': 1.1210085591373455, 'shape': 0.9831957705294134}
for year in range(1, 3):
coordinate = np.array([0.0, 0.0, year])
- mle_params_estimated = estimator.margin_function_from_fit.get_gev_params(coordinate).to_dict()
+ mle_params_estimated = estimator.function_from_fit.get_gev_params(coordinate).to_dict()
for key in ref.keys():
self.assertAlmostEqual(ref[key], mle_params_estimated[key], places=3)
@@ -47,32 +47,32 @@ class TestMaxStableTemporal(unittest.TestCase):
estimator = FullEstimatorInASingleStepWithSmoothMargin(self.dataset, margin_model,
self.max_stable_model)
estimator.fit()
- self.assertNotEqual(estimator.margin_function_from_fit.mu1_temporal_trend, 0.0)
+ self.assertNotEqual(estimator.function_from_fit.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_from_fit.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1).to_dict()
coordinate3 = np.array([0.0, 0.0, 3])
- mle_params_estimated_year3 = estimator.margin_function_from_fit.get_gev_params(coordinate3).to_dict()
+ mle_params_estimated_year3 = estimator.function_from_fit.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)
# By default, estimator find the good margin
- self.assertNotEqual(estimator.margin_function_from_fit.mu1_temporal_trend, 0.0)
- self.assertAlmostEqual(estimator.margin_function_from_fit.mu1_temporal_trend,
+ self.assertNotEqual(estimator.function_from_fit.mu1_temporal_trend, 0.0)
+ self.assertAlmostEqual(estimator.function_from_fit.mu1_temporal_trend,
self.smooth_margin_model.margin_function_sample.mu1_temporal_trend,
places=2)
# Checks starting point parameter are well passed
- self.assertEqual(2, estimator.margin_function_from_fit.starting_point)
+ self.assertEqual(2, estimator.function_from_fit.starting_point)
# Checks that parameters returned are indeed different
coordinate1 = np.array([0.0, 0.0, 1])
- mle_params_estimated_year1 = estimator.margin_function_from_fit.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.function_from_fit.get_gev_params(coordinate1).to_dict()
coordinate2 = np.array([0.0, 0.0, 2])
- mle_params_estimated_year2 = estimator.margin_function_from_fit.get_gev_params(coordinate2).to_dict()
+ mle_params_estimated_year2 = estimator.function_from_fit.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_from_fit.get_gev_params(coordinate5).to_dict()
+ mle_params_estimated_year5 = estimator.function_from_fit.get_gev_params(coordinate5).to_dict()
self.assertNotEqual(mle_params_estimated_year5, mle_params_estimated_year2)
def fit_non_stationary_estimator(self, starting_point):
@@ -86,8 +86,8 @@ class TestMaxStableTemporal(unittest.TestCase):
# 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_from_fit.mu1_temporal_trend
- mu1_estimator2 = estimator2.margin_function_from_fit.mu1_temporal_trend
+ mu1_estimator1 = estimator1.function_from_fit.mu1_temporal_trend
+ mu1_estimator2 = estimator2.function_from_fit.mu1_temporal_trend
self.assertNotEqual(mu1_estimator1, mu1_estimator2)
--
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