From 92cb2f8d909468a28c81d7498982d3f56acc2487 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Wed, 16 Oct 2019 15:20:19 +0200
Subject: [PATCH] [EXTREME ESTIMATOR] refactor result from model fit. refactor
estimator.
---
.../study_visualization/study_visualizer.py | 2 +-
.../stations_data/comparison_analysis.py | 6 +-
.../comparisons_visualization.py | 6 -
.../regression_margin/regression_margin.py | 2 +-
.../max_stable_process_plot.py | 6 +-
.../spatial_robustness/alps_msp_robustness.py | 3 +-
.../unidimensional_robustness.py | 1 -
experiment/simulation/abstract_simulation.py | 2 +-
.../trend_analysis/non_stationary_trends.py | 6 +-
.../abstract_gev_trend_test.py | 18 +-
.../estimator/abstract_estimator.py | 36 ++--
.../full_estimator/abstract_full_estimator.py | 20 ++-
.../abstract_margin_estimator.py | 17 +-
.../abstract_max_stable_estimator.py | 22 +--
extreme_estimator/estimator/utils.py | 10 ++
.../margin_model/abstract_margin_model.py | 4 +-
.../abstract_temporal_linear_margin_model.py | 44 +++--
.../margin_model/parametric_margin_model.py | 8 +-
.../abstract_max_stable_model.py | 4 +-
.../extreme_models/result_from_fit.py | 168 ------------------
.../result_from_model_fit/__init__.py | 0
.../abstract_result_from_model_fit.py | 45 +++++
.../result_from_extremes.py | 52 ++++++
.../result_from_ismev.py | 54 ++++++
.../result_from_spatial_extreme.py | 41 +++++
.../result_from_model_fit/utils.py | 5 +
extreme_estimator/extreme_models/utils.py | 10 ++
.../margin_fits/margin_fits_utils.py | 4 +-
.../test_coordinate_sensitivity.py | 12 +-
.../test_estimator/test_margin_estimators.py | 4 +-
.../test_margin_temporal.py | 24 +--
.../test_margin_temporal_transformed.py | 32 ++--
.../test_max_stable_temporal.py | 24 +--
.../test_gev/test_gev_temporal.py | 20 +--
.../test_fitmaxstab_with_margin.py | 4 +-
35 files changed, 381 insertions(+), 335 deletions(-)
create mode 100644 extreme_estimator/estimator/utils.py
delete mode 100644 extreme_estimator/extreme_models/result_from_fit.py
create mode 100644 extreme_estimator/extreme_models/result_from_model_fit/__init__.py
create mode 100644 extreme_estimator/extreme_models/result_from_model_fit/abstract_result_from_model_fit.py
create mode 100644 extreme_estimator/extreme_models/result_from_model_fit/result_from_extremes.py
create mode 100644 extreme_estimator/extreme_models/result_from_model_fit/result_from_ismev.py
create mode 100644 extreme_estimator/extreme_models/result_from_model_fit/result_from_spatial_extreme.py
create mode 100644 extreme_estimator/extreme_models/result_from_model_fit/utils.py
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 be39035a..80016f96 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
@@ -722,7 +722,7 @@ class StudyVisualizer(VisualizationParameters):
estimator.fit()
# Set visualization attributes for margin_fct
- margin_fct = estimator.margin_function_fitted
+ margin_fct = estimator.margin_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/meteo_france_data/stations_data/comparison_analysis.py b/experiment/meteo_france_data/stations_data/comparison_analysis.py
index dab4fe2b..78bdff75 100644
--- a/experiment/meteo_france_data/stations_data/comparison_analysis.py
+++ b/experiment/meteo_france_data/stations_data/comparison_analysis.py
@@ -280,11 +280,11 @@ class ComparisonAnalysis(object):
margin_model=margin_model,
max_stable_model=max_stable_model)
estimator.fit()
- print(estimator.result_from_fit.margin_coef_dict)
- print(estimator.result_from_fit.other_coef_dict)
+ print(estimator.result_from_model_fit.margin_coef_dict)
+ print(estimator.result_from_model_fit.other_coef_dict)
estimators.append(estimator)
# Compare the sign of them margin coefficient for the estimators
- coefs = [{k: v for k, v in e.result_from_fit.margin_coef_dict.items() if 'Coeff1' not in k} for e in
+ coefs = [{k: v for k, v in e.result_from_model_fit.margin_coef_dict.items() if 'Coeff1' not in k} for e in
estimators]
different_sign = [k for k, v in coefs[0].items() if np.sign(coefs[1][k]) != np.sign(v)]
print('All linear coefficient have the same sign: {}, different_signs for: {}'.format(
diff --git a/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py b/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py
index 5ee81c4f..af0fd9b5 100644
--- a/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py
+++ b/experiment/meteo_france_data/stations_data/visualization/comparisons_visualization/comparisons_visualization.py
@@ -16,13 +16,7 @@ from experiment.meteo_france_data.stations_data.comparison_analysis import Compa
REANALYSE_STR, ALTITUDE_COLUMN_NAME, STATION_COLUMN_NAME
from experiment.trend_analysis.univariate_test.abstract_univariate_test import AbstractUnivariateTest
from experiment.trend_analysis.univariate_test.utils import compute_gev_change_point_test_results
-from extreme_estimator.extreme_models.result_from_fit import ResultFromIsmev
-from extreme_estimator.extreme_models.utils import r, safe_run_r_estimator, ro
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
-from utils import classproperty
-
-
-
path = r'/home/erwan/Documents/projects/spatiotemporalextremes/experiment/meteo_france_data/stations_data/csv'
path_last_csv_file = op.join(path, 'example.csv')
diff --git a/experiment/regression_margin/regression_margin.py b/experiment/regression_margin/regression_margin.py
index 18cc9a57..b16bd28a 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_fitted.visualize_function(axes=axes, show=False)
+ full_estimator.margin_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/robustness_plot/estimation_robustness/max_stable_process_plot.py b/experiment/robustness_plot/estimation_robustness/max_stable_process_plot.py
index b98a7b44..3be6ef87 100644
--- a/experiment/robustness_plot/estimation_robustness/max_stable_process_plot.py
+++ b/experiment/robustness_plot/estimation_robustness/max_stable_process_plot.py
@@ -40,7 +40,11 @@ class MaxStableProcessPlot(object):
coordinates=spatial_coordinates)
estimator = MaxStableEstimator(dataset, max_stable_model)
estimator.fit()
- return estimator.scalars(max_stable_model.params_sample)
+ # Estimator was computing some error as follows:
+ # absolute_errors = {param_name: np.abs(param_true_value - self.max_stable_params_fitted[param_name])
+ # for param_name, param_true_value in true_max_stable_params.items()}
+ # mean_absolute_error = np.mean(np.array(list(absolute_errors.values())))
+ # return estimator.scalars(max_stable_model.params_sample)
class SingleMaxStableProcessPlot(SinglePlot, MaxStableProcessPlot):
diff --git a/experiment/robustness_plot/estimation_robustness/spatial_robustness/alps_msp_robustness.py b/experiment/robustness_plot/estimation_robustness/spatial_robustness/alps_msp_robustness.py
index 8e148738..e8a7e84b 100644
--- a/experiment/robustness_plot/estimation_robustness/spatial_robustness/alps_msp_robustness.py
+++ b/experiment/robustness_plot/estimation_robustness/spatial_robustness/alps_msp_robustness.py
@@ -1,4 +1,3 @@
-from extreme_estimator.estimator.abstract_estimator import AbstractEstimator
from extreme_estimator.extreme_models.max_stable_model.max_stable_models import Smith, BrownResnick
from experiment.robustness_plot.estimation_robustness.max_stable_process_plot import MultipleMaxStableProcessPlot, MaxStableProcessPlot
from experiment.robustness_plot.single_plot import SinglePlot
@@ -45,7 +44,7 @@ def multiple_spatial_robustness_alps():
# Put only the parameter that will vary
spatial_robustness.robustness_grid_plot(**{
- SinglePlot.OrdinateItem.name: [AbstractEstimator.MAE_ERROR, AbstractEstimator.DURATION],
+ # SinglePlot.OrdinateItem.name: [AbstractEstimator.MAE_ERROR, AbstractEstimator.DURATION],
MaxStableProcessPlot.NbStationItem.name: nb_stations,
MaxStableProcessPlot.NbObservationItem.name: nb_observation,
MaxStableProcessPlot.MaxStableModelItem.name: msp_models,
diff --git a/experiment/robustness_plot/estimation_robustness/unidimensional_robustness/unidimensional_robustness.py b/experiment/robustness_plot/estimation_robustness/unidimensional_robustness/unidimensional_robustness.py
index 937a65c9..ad55216a 100644
--- a/experiment/robustness_plot/estimation_robustness/unidimensional_robustness/unidimensional_robustness.py
+++ b/experiment/robustness_plot/estimation_robustness/unidimensional_robustness/unidimensional_robustness.py
@@ -1,4 +1,3 @@
-from extreme_estimator.estimator.abstract_estimator import AbstractEstimator
from extreme_estimator.extreme_models.max_stable_model.max_stable_models import Smith, BrownResnick
from experiment.robustness_plot.estimation_robustness.max_stable_process_plot import MultipleMaxStableProcessPlot, MaxStableProcessPlot
from experiment.robustness_plot.single_plot import SinglePlot
diff --git a/experiment/simulation/abstract_simulation.py b/experiment/simulation/abstract_simulation.py
index 22f6ee32..8cd1c03c 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_fitted)
+ margin_function_fitted_list.append(estimator.margin_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 0334c3c2..6f532ed2 100644
--- a/experiment/trend_analysis/non_stationary_trends.py
+++ b/experiment/trend_analysis/non_stationary_trends.py
@@ -59,7 +59,7 @@ class AbstractNonStationaryTrendTest(object):
text = 'Fittig {} with margin: {} for starting_point={}\n'.format(estimator_name,
margin_model_name,
starting_point)
- text += 'Fit took {}s and was {}'.format(round(duration, 1), estimator.result_from_fit.convergence)
+ text += 'Fit took {}s and was {}'.format(round(duration, 1), estimator.result_from_model_fit.convergence)
print(text)
return estimator
@@ -68,14 +68,14 @@ class AbstractNonStationaryTrendTest(object):
def get_metric(self, starting_point):
estimator = self.get_estimator(starting_point)
- metric = estimator.result_from_fit.__getattribute__(self.RESULT_ATTRIBUTE_METRIC)
+ metric = estimator.result_from_model_fit.__getattribute__(self.RESULT_ATTRIBUTE_METRIC)
assert isinstance(metric, float)
return metric
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_fitted # type: LinearMarginFunction
+ margin_function = estimator.margin_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/abstract_gev_trend_test.py b/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py
index f9a59b27..edea9fdd 100644
--- a/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py
+++ b/experiment/trend_analysis/univariate_test/abstract_gev_trend_test.py
@@ -89,21 +89,21 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
if self.crashed:
return np.nan
else:
- return self.constrained_estimator.result_from_fit.deviance
+ return self.constrained_estimator.result_from_model_fit.deviance
@property
def unconstrained_model_deviance(self):
if self.crashed:
return np.nan
else:
- return self.unconstrained_estimator.result_from_fit.deviance
+ return self.unconstrained_estimator.result_from_model_fit.deviance
@property
def unconstained_nllh(self):
if self.crashed:
return np.nan
else:
- return self.unconstrained_estimator.result_from_fit.nllh
+ return self.unconstrained_estimator.result_from_model_fit.nllh
# Evolution of the GEV parameters and corresponding quantiles
@@ -112,20 +112,20 @@ class AbstractGevTrendTest(AbstractUnivariateTest):
return np.sign(self.test_trend_slope_strength)
def get_non_stationary_linear_coef(self, gev_param_name: str):
- return self.unconstrained_estimator.margin_function_fitted.get_coef(gev_param_name,
- AbstractCoordinates.COORDINATE_T)
+ return self.unconstrained_estimator.margin_function_from_fit.get_coef(gev_param_name,
+ AbstractCoordinates.COORDINATE_T)
@cached_property
def non_stationary_constant_gev_params(self) -> GevParams:
# Constant parameters correspond to the gev params in 1958
- return self.unconstrained_estimator.margin_function_fitted.get_gev_params(coordinate=np.array([1958]),
- is_transformed=False)
+ return self.unconstrained_estimator.margin_function_from_fit.get_gev_params(coordinate=np.array([1958]),
+ is_transformed=False)
@cached_property
def stationary_constant_gev_params(self) -> GevParams:
# Constant parameters correspond to any gev params
- return self.constrained_estimator.margin_function_fitted.get_gev_params(coordinate=np.array([1958]),
- is_transformed=False)
+ return self.constrained_estimator.margin_function_from_fit.get_gev_params(coordinate=np.array([1958]),
+ is_transformed=False)
@property
diff --git a/extreme_estimator/estimator/abstract_estimator.py b/extreme_estimator/estimator/abstract_estimator.py
index 2becbbfb..8d13038b 100644
--- a/extreme_estimator/estimator/abstract_estimator.py
+++ b/extreme_estimator/estimator/abstract_estimator.py
@@ -1,12 +1,10 @@
-import time
+from typing import Union
from cached_property import cached_property
-from extreme_estimator.extreme_models.margin_model.linear_margin_model.linear_margin_model import LinearMarginModel
-from extreme_estimator.extreme_models.result_from_fit import ResultFromFit
+from extreme_estimator.extreme_models.result_from_model_fit.abstract_result_from_model_fit import AbstractResultFromModelFit
from extreme_estimator.extreme_models.margin_model.margin_function.abstract_margin_function import \
AbstractMarginFunction
-from extreme_estimator.extreme_models.margin_model.margin_function.linear_margin_function import LinearMarginFunction
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
@@ -14,34 +12,38 @@ class AbstractEstimator(object):
def __init__(self, dataset: AbstractDataset):
self.dataset = dataset # type: AbstractDataset
- self._result_from_fit = None # type: ResultFromFit
+ self._result_from_fit = None # type: Union[None, AbstractResultFromModelFit]
+ # Class constructor
@classmethod
def from_dataset(cls, dataset: AbstractDataset):
raise NotImplementedError
+ # Fit estimator
+
def fit(self):
+ self._result_from_fit = self._fit()
+
+ def _fit(self) -> AbstractResultFromModelFit:
raise NotImplementedError
+ # Results from model fit
+
@property
- def result_from_fit(self) -> ResultFromFit:
- assert self._result_from_fit is not None, 'Fit has not be done'
+ def result_from_model_fit(self) -> AbstractResultFromModelFit:
+ assert self._result_from_fit is not None, 'Estimator has not been fitted'
return self._result_from_fit
@cached_property
- def margin_function_fitted(self) -> AbstractMarginFunction:
- return self.extract_function_fitted()
-
- def extract_function_fitted(self) -> AbstractMarginFunction:
+ def margin_function_from_fit(self) -> AbstractMarginFunction:
raise NotImplementedError
- def extract_function_fitted_from_the_model_shape(self, margin_model: LinearMarginModel):
- return LinearMarginFunction.from_coef_dict(coordinates=self.dataset.coordinates,
- gev_param_name_to_dims=margin_model.margin_function_start_fit.gev_param_name_to_dims,
- coef_dict=self.result_from_fit.margin_coef_dict,
- starting_point=margin_model.starting_point)
+ # Short cut properties
- #
@property
def train_split(self):
return self.dataset.train_split
+
+
+
+
diff --git a/extreme_estimator/estimator/full_estimator/abstract_full_estimator.py b/extreme_estimator/estimator/full_estimator/abstract_full_estimator.py
index 85fcb135..8185ba10 100644
--- a/extreme_estimator/estimator/full_estimator/abstract_full_estimator.py
+++ b/extreme_estimator/estimator/full_estimator/abstract_full_estimator.py
@@ -3,6 +3,7 @@ from cached_property import cached_property
from extreme_estimator.estimator.abstract_estimator import AbstractEstimator
from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
from extreme_estimator.estimator.max_stable_estimator.abstract_max_stable_estimator import MaxStableEstimator
+from extreme_estimator.estimator.utils import load_margin_function
from extreme_estimator.extreme_models.margin_model.abstract_margin_model import AbstractMarginModel
from extreme_estimator.extreme_models.margin_model.margin_function.linear_margin_function import LinearMarginFunction
from extreme_estimator.extreme_models.margin_model.linear_margin_model.linear_margin_model import LinearMarginModel
@@ -33,12 +34,18 @@ 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_fitted)
+ margin_function=self.margin_estimator.margin_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
self.max_stable_estimator.fit()
+ """
+ To clean things, I could let an abstract Estimator whose only function is fit
+ then create a sub class abstract Estimator One Model, where the function _fit exists and always return something
+ then here full estimator class will be a sub class of abstract estimator, with two abstract Estimator One Model as attributes
+ """
+
class FullEstimatorInASingleStep(AbstractFullEstimator):
pass
@@ -67,9 +74,9 @@ class FullEstimatorInASingleStepWithSmoothMargin(AbstractFullEstimator):
return self.dataset.coordinates.df_temporal_coordinates_for_fit(split=self.train_split,
starting_point=self.linear_margin_model.starting_point)
- def fit(self):
+ def _fit(self):
# Estimate both the margin and the max-stable structure
- self._result_from_fit = self.max_stable_model.fitmaxstab(
+ return self.max_stable_model.fitmaxstab(
data_gev=self.dataset.maxima_gev_for_spatial_extremes_package(self.train_split),
df_coordinates_spat=self.df_coordinates_spat,
df_coordinates_temp=self.df_coordinates_temp,
@@ -78,12 +85,9 @@ class FullEstimatorInASingleStepWithSmoothMargin(AbstractFullEstimator):
margin_start_dict=self.linear_margin_model.margin_function_start_fit.coef_dict
)
- def extract_function_fitted(self):
- return self.extract_function_fitted_from_the_model_shape(self.linear_margin_model)
-
@cached_property
- def margin_function_fitted(self) -> LinearMarginFunction:
- return super().margin_function_fitted
+ def margin_function_from_fit(self) -> LinearMarginFunction:
+ return load_margin_function(self, self.linear_margin_model)
class PointwiseAndThenUnitaryMsp(AbstractFullEstimator):
diff --git a/extreme_estimator/estimator/margin_estimator/abstract_margin_estimator.py b/extreme_estimator/estimator/margin_estimator/abstract_margin_estimator.py
index afdf32c8..be13d011 100644
--- a/extreme_estimator/estimator/margin_estimator/abstract_margin_estimator.py
+++ b/extreme_estimator/estimator/margin_estimator/abstract_margin_estimator.py
@@ -3,8 +3,10 @@ from abc import ABC
from cached_property import cached_property
from extreme_estimator.estimator.abstract_estimator import AbstractEstimator
+from extreme_estimator.estimator.utils import load_margin_function
from extreme_estimator.extreme_models.margin_model.linear_margin_model.linear_margin_model import LinearMarginModel
from extreme_estimator.extreme_models.margin_model.margin_function.linear_margin_function import LinearMarginFunction
+from extreme_estimator.extreme_models.result_from_model_fit.abstract_result_from_model_fit import AbstractResultFromModelFit
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
@@ -14,29 +16,24 @@ class AbstractMarginEstimator(AbstractEstimator, ABC):
super().__init__(dataset)
assert self.dataset.maxima_gev() is not None
+
class LinearMarginEstimator(AbstractMarginEstimator):
"""# with different type of marginals: cosntant, linear...."""
- def _error(self, true_max_stable_params: dict):
- pass
-
def __init__(self, dataset: AbstractDataset, margin_model: LinearMarginModel):
super().__init__(dataset)
assert isinstance(margin_model, LinearMarginModel)
self.margin_model = margin_model
- def fit(self):
+ def _fit(self) -> AbstractResultFromModelFit:
maxima_gev_specialized = self.dataset.maxima_gev_for_spatial_extremes_package(self.train_split)
df_coordinates_spat = self.dataset.coordinates.df_spatial_coordinates(self.train_split)
df_coordinates_temp = self.dataset.coordinates.df_temporal_coordinates_for_fit(split=self.train_split,
starting_point=self.margin_model.starting_point)
- self._result_from_fit = self.margin_model.fitmargin_from_maxima_gev(data=maxima_gev_specialized,
+ return self.margin_model.fitmargin_from_maxima_gev(data=maxima_gev_specialized,
df_coordinates_spat=df_coordinates_spat,
df_coordinates_temp=df_coordinates_temp)
@cached_property
- def margin_function_fitted(self) -> LinearMarginFunction:
- return super().margin_function_fitted
-
- def extract_function_fitted(self) -> LinearMarginFunction:
- return self.extract_function_fitted_from_the_model_shape(self.margin_model)
+ def margin_function_from_fit(self) -> LinearMarginFunction:
+ return load_margin_function(self, self.margin_model)
diff --git a/extreme_estimator/estimator/max_stable_estimator/abstract_max_stable_estimator.py b/extreme_estimator/estimator/max_stable_estimator/abstract_max_stable_estimator.py
index 8a9bed24..a824b422 100644
--- a/extreme_estimator/estimator/max_stable_estimator/abstract_max_stable_estimator.py
+++ b/extreme_estimator/estimator/max_stable_estimator/abstract_max_stable_estimator.py
@@ -10,25 +10,19 @@ class AbstractMaxStableEstimator(AbstractEstimator):
def __init__(self, dataset: AbstractDataset, max_stable_model: AbstractMaxStableModel):
super().__init__(dataset=dataset)
self.max_stable_model = max_stable_model
- # Fit parameters
- self.max_stable_params_fitted = None
+ @property
+ def max_stable_params_fitted(self):
+ raise NotImplementedError
class MaxStableEstimator(AbstractMaxStableEstimator):
- def fit(self):
+ def _fit(self):
assert self.dataset.maxima_frech(split=self.train_split) is not None
- self._result_from_fit = self.max_stable_model.fitmaxstab(
+ return self.max_stable_model.fitmaxstab(
data_frech=self.dataset.maxima_frech_for_spatial_extremes_package(split=self.train_split),
df_coordinates_spat=self.dataset.df_coordinates(split=self.train_split))
- self.max_stable_params_fitted = self.result_from_fit.all_parameters
- def scalars(self, true_max_stable_params: dict):
- error = self._error(true_max_stable_params)
- return {**error}
-
- def _error(self, true_max_stable_params: dict):
- absolute_errors = {param_name: np.abs(param_true_value - self.max_stable_params_fitted[param_name])
- for param_name, param_true_value in true_max_stable_params.items()}
- mean_absolute_error = np.mean(np.array(list(absolute_errors.values())))
- return {**absolute_errors, **{self.MAE_ERROR: mean_absolute_error}}
+ @property
+ def max_stable_params_fitted(self):
+ return self.result_from_model_fit.all_parameters
diff --git a/extreme_estimator/estimator/utils.py b/extreme_estimator/estimator/utils.py
new file mode 100644
index 00000000..f14fd7f4
--- /dev/null
+++ b/extreme_estimator/estimator/utils.py
@@ -0,0 +1,10 @@
+from extreme_estimator.estimator.abstract_estimator import AbstractEstimator
+from extreme_estimator.extreme_models.margin_model.linear_margin_model.linear_margin_model import LinearMarginModel
+from extreme_estimator.extreme_models.margin_model.margin_function.linear_margin_function import LinearMarginFunction
+
+
+def load_margin_function(estimator: AbstractEstimator, margin_model: LinearMarginModel, margin_function_class=LinearMarginFunction):
+ return margin_function_class.from_coef_dict(coordinates=estimator.dataset.coordinates,
+ gev_param_name_to_dims=margin_model.margin_function_start_fit.gev_param_name_to_dims,
+ coef_dict=estimator.result_from_model_fit.margin_coef_dict,
+ starting_point=margin_model.starting_point)
diff --git a/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py b/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py
index 7e44d0dc..7db9d3d8 100644
--- a/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py
@@ -6,7 +6,7 @@ import pandas as pd
from extreme_estimator.extreme_models.abstract_model import AbstractModel
from extreme_estimator.extreme_models.margin_model.margin_function.abstract_margin_function \
import AbstractMarginFunction
-from extreme_estimator.extreme_models.result_from_fit import ResultFromFit
+from extreme_estimator.extreme_models.result_from_model_fit.abstract_result_from_model_fit import AbstractResultFromModelFit
from extreme_estimator.extreme_models.utils import r
from extreme_estimator.margin_fits.gev.gev_params import GevParams
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -79,7 +79,7 @@ class AbstractMarginModel(AbstractModel, ABC):
# Fitting methods needs to be defined in child classes
def fitmargin_from_maxima_gev(self, maxima_gev: np.ndarray, df_coordinates_spatial: pd.DataFrame,
- df_coordinates_temporal: pd.DataFrame) -> ResultFromFit:
+ df_coordinates_temporal: pd.DataFrame) -> AbstractResultFromModelFit:
raise NotImplementedError
diff --git a/extreme_estimator/extreme_models/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py b/extreme_estimator/extreme_models/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
index 5f3eefff..67c2ffea 100644
--- a/extreme_estimator/extreme_models/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
@@ -2,10 +2,11 @@ import numpy as np
import pandas as pd
from extreme_estimator.extreme_models.margin_model.linear_margin_model.linear_margin_model import LinearMarginModel
-from extreme_estimator.extreme_models.result_from_fit import ResultFromFit, ResultFromIsmev, ResultFromExtremes
+from extreme_estimator.extreme_models.result_from_model_fit.abstract_result_from_model_fit import AbstractResultFromModelFit
+from extreme_estimator.extreme_models.result_from_model_fit.result_from_extremes import ResultFromExtremes
+from extreme_estimator.extreme_models.result_from_model_fit.result_from_ismev import ResultFromIsmev
from extreme_estimator.extreme_models.utils import r, ro, get_null
from extreme_estimator.extreme_models.utils import safe_run_r_estimator
-from extreme_estimator.margin_fits.gev.gev_params import GevParams
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -15,33 +16,47 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
EXTREMES_FEVD_BAYESIAN_FIT_METHOD_STR = 'extRemes.fevd.Bayesian'
def __init__(self, coordinates: AbstractCoordinates, use_start_value=False, params_start_fit=None,
- params_sample=None, starting_point=None, fit_method='isMev.gev.fit'):
+ params_sample=None, starting_point=None, fit_method=ISMEV_GEV_FIT_METHOD_STR):
super().__init__(coordinates, use_start_value, params_start_fit, params_sample, starting_point)
assert fit_method in [self.ISMEV_GEV_FIT_METHOD_STR, self.EXTREMES_FEVD_BAYESIAN_FIT_METHOD_STR]
self.fit_method = fit_method
def fitmargin_from_maxima_gev(self, data: np.ndarray, df_coordinates_spat: pd.DataFrame,
- df_coordinates_temp: pd.DataFrame) -> ResultFromFit:
+ df_coordinates_temp: pd.DataFrame) -> AbstractResultFromModelFit:
assert data.shape[1] == len(df_coordinates_temp.values)
+ x = ro.FloatVector(data[0])
+ y = df_coordinates_temp.values
if self.fit_method == self.ISMEV_GEV_FIT_METHOD_STR:
- return self.ismev_gev_fit(data, df_coordinates_temp)
+ return self.ismev_gev_fit(x, y)
if self.fit_method == self.EXTREMES_FEVD_BAYESIAN_FIT_METHOD_STR:
- return self.extremes_fevd_bayesian_fit(data, df_coordinates_temp)
+ return self.extremes_fevd_bayesian_fit(x, y)
# Gev Fit with isMev package
- def ismev_gev_fit(self, data: np.ndarray, df_coordinates_temp: pd.DataFrame) -> ResultFromIsmev:
+ def ismev_gev_fit(self, x, y) -> ResultFromIsmev:
res = safe_run_r_estimator(function=r('gev.fit'), use_start=self.use_start_value,
- xdat=ro.FloatVector(data[0]), y=df_coordinates_temp.values, mul=self.mul,
+ xdat=x, y=y, mul=self.mul,
sigl=self.sigl, shl=self.shl)
return ResultFromIsmev(res, self.margin_function_start_fit.gev_param_name_to_dims)
# Gev fit with extRemes package
- def extremes_fevd_bayesian_fit(self, data, df_coordinates_temp) -> ResultFromExtremes:
- res = safe_run_r_estimator(function=r('fevd_fixed'), use_start=self.use_start_value,
- xdat=ro.FloatVector(data[0]), y=df_coordinates_temp.values, mul=self.mul,
- sigl=self.sigl, shl=self.shl)
+ def extremes_fevd_bayesian_fit(self, x, y) -> ResultFromExtremes:
+ # (x_gev, method='Bayesian', priorFun="fevdPriorMyMy", priorParams=list(q=c(6), p = c(
+ # 9)), iter = 5000, verbose = TRUE, use.phi = FALSE)
+
+ r_type_argument_kwargs = {'use.phi': False}
+ # location.fun = ~1,
+ # scale.fun = ~1, shape.fun = ~1
+ # , priorParams = list(q=c(6), p=c(9))
+ res = safe_run_r_estimator(function=r('fevd_fixed'),
+ x=x,
+ data=y,
+ method='Bayesian',
+ # priorFun="fevdPriorCustom",
+ iter=5000,
+ **r_type_argument_kwargs
+ )
return ResultFromExtremes(res, self.margin_function_start_fit.gev_param_name_to_dims)
# Default arguments for all methods
@@ -61,8 +76,3 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
@property
def siglink(self):
return r('identity')
-
-
-
-
-
diff --git a/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py b/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
index e9241c45..be503f03 100644
--- a/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
@@ -5,15 +5,11 @@ import pandas as pd
from extreme_estimator.extreme_models.margin_model.margin_function.parametric_margin_function import \
ParametricMarginFunction
-from extreme_estimator.extreme_models.result_from_fit import ResultFromFit, ResultFromSpatialExtreme
+from extreme_estimator.extreme_models.result_from_model_fit.result_from_spatial_extreme import ResultFromSpatialExtreme
from extreme_estimator.extreme_models.margin_model.abstract_margin_model import AbstractMarginModel
from extreme_estimator.extreme_models.utils import safe_run_r_estimator, r, get_coord, \
get_margin_formula
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
-from spatio_temporal_dataset.coordinates.spatio_temporal_coordinates.abstract_spatio_temporal_coordinates import \
- AbstractSpatioTemporalCoordinates
-from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
- AbstractTemporalCoordinates
class ParametricMarginModel(AbstractMarginModel, ABC):
@@ -29,7 +25,7 @@ class ParametricMarginModel(AbstractMarginModel, ABC):
super().__init__(coordinates, use_start_value, params_start_fit, params_sample)
def fitmargin_from_maxima_gev(self, data: np.ndarray, df_coordinates_spat: pd.DataFrame,
- df_coordinates_temp: pd.DataFrame) -> ResultFromFit:
+ df_coordinates_temp: pd.DataFrame) -> ResultFromSpatialExtreme:
assert data.shape[1] == len(df_coordinates_spat)
# Margin formula for fitspatgev
diff --git a/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py b/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py
index 57cd069e..b1fc374d 100644
--- a/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py
+++ b/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py
@@ -6,7 +6,7 @@ from rpy2.rinterface import RRuntimeWarning
from rpy2.rinterface._rinterface import RRuntimeError
from extreme_estimator.extreme_models.abstract_model import AbstractModel
-from extreme_estimator.extreme_models.result_from_fit import ResultFromFit, ResultFromSpatialExtreme
+from extreme_estimator.extreme_models.result_from_model_fit.result_from_spatial_extreme import ResultFromSpatialExtreme
from extreme_estimator.extreme_models.utils import r, safe_run_r_estimator, get_coord, \
get_margin_formula, SafeRunException
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -24,7 +24,7 @@ class AbstractMaxStableModel(AbstractModel):
def fitmaxstab(self, df_coordinates_spat: pd.DataFrame, df_coordinates_temp: pd.DataFrame = None,
data_frech: np.ndarray = None, data_gev: np.ndarray = None,
- fit_marge=False, fit_marge_form_dict=None, margin_start_dict=None) -> ResultFromFit:
+ fit_marge=False, fit_marge_form_dict=None, margin_start_dict=None) -> ResultFromSpatialExtreme:
assert isinstance(df_coordinates_spat, pd.DataFrame)
if fit_marge:
assert fit_marge_form_dict is not None
diff --git a/extreme_estimator/extreme_models/result_from_fit.py b/extreme_estimator/extreme_models/result_from_fit.py
deleted file mode 100644
index 212ce05f..00000000
--- a/extreme_estimator/extreme_models/result_from_fit.py
+++ /dev/null
@@ -1,168 +0,0 @@
-from typing import Dict
-
-import numpy as np
-from rpy2 import robjects
-
-from extreme_estimator.extreme_models.margin_model.param_function.linear_coef import LinearCoef
-from extreme_estimator.margin_fits.gev.gev_params import GevParams
-from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
-
-
-def convertFloatVector_to_float(f):
- return np.array(f)[0]
-
-
-class ResultFromFit(object):
-
- def __init__(self, result_from_fit: robjects.ListVector) -> None:
- if hasattr(result_from_fit, 'names'):
- self.name_to_value = {name: result_from_fit.rx2(name) for name in result_from_fit.names}
- else:
- self.name_to_value = {}
-
- @property
- def names(self):
- return self.name_to_value.keys()
-
- @property
- def all_parameters(self):
- raise NotImplementedError
-
- @property
- def margin_coef_dict(self):
- raise NotImplementedError
-
- @property
- def other_coef_dict(self):
- raise NotImplementedError
-
- @property
- def nllh(self):
- raise NotImplementedError
-
- @property
- def deviance(self):
- raise NotImplementedError
-
- @property
- def convergence(self) -> str:
- raise NotImplementedError
-
-
-class ResultFromIsmev(ResultFromFit):
-
- def __init__(self, result_from_fit: robjects.ListVector, gev_param_name_to_dim=None) -> None:
- super().__init__(result_from_fit)
- self.gev_param_name_to_dim = gev_param_name_to_dim
-
- @property
- def margin_coef_dict(self):
- assert self.gev_param_name_to_dim is not None
- # Build the Coeff dict from gev_param_name_to_dim
- coef_dict = {}
- i = 0
- mle_values = self.name_to_value['mle']
- for gev_param_name in GevParams.PARAM_NAMES:
- # Add intercept
- intercept_coef_name = LinearCoef.coef_template_str(gev_param_name, LinearCoef.INTERCEPT_NAME).format(1)
- coef_dict[intercept_coef_name] = mle_values[i]
- i += 1
- # Add a potential linear temporal trend
- if gev_param_name in self.gev_param_name_to_dim:
- temporal_coef_name = LinearCoef.coef_template_str(gev_param_name,
- AbstractCoordinates.COORDINATE_T).format(1)
- coef_dict[temporal_coef_name] = mle_values[i]
- i += 1
- return coef_dict
-
- @property
- def all_parameters(self):
- return self.margin_coef_dict
-
- @property
- def nllh(self):
- return convertFloatVector_to_float(self.name_to_value['nllh'])
-
- @property
- def deviance(self):
- return - 2 * self.nllh
-
- @property
- def convergence(self) -> str:
- return convertFloatVector_to_float(self.name_to_value['conv']) == 0
-
-
-class ResultFromExtremes(ResultFromFit):
-
- def __init__(self, result_from_fit: robjects.ListVector, gev_param_name_to_dim=None) -> None:
- super().__init__(result_from_fit)
- self.gev_param_name_to_dim = gev_param_name_to_dim
-
- @property
- def margin_coef_dict(self):
- assert self.gev_param_name_to_dim is not None
- # Build the Coeff dict from gev_param_name_to_dim
- coef_dict = {}
- i = 0
- mle_values = self.name_to_value['mle']
- for gev_param_name in GevParams.PARAM_NAMES:
- # Add intercept
- intercept_coef_name = LinearCoef.coef_template_str(gev_param_name, LinearCoef.INTERCEPT_NAME).format(1)
- coef_dict[intercept_coef_name] = mle_values[i]
- i += 1
- # Add a potential linear temporal trend
- if gev_param_name in self.gev_param_name_to_dim:
- temporal_coef_name = LinearCoef.coef_template_str(gev_param_name,
- AbstractCoordinates.COORDINATE_T).format(1)
- coef_dict[temporal_coef_name] = mle_values[i]
- i += 1
- return coef_dict
-
- @property
- def all_parameters(self):
- return self.margin_coef_dict
-
- @property
- def nllh(self):
- return convertFloatVector_to_float(self.name_to_value['nllh'])
-
- @property
- def deviance(self):
- return - 2 * self.nllh
-
- @property
- def convergence(self) -> str:
- return convertFloatVector_to_float(self.name_to_value['conv']) == 0
-
-class ResultFromSpatialExtreme(ResultFromFit):
- """
- Handler from any result with the result of a fit functions from the package Spatial Extreme
- """
- FITTED_VALUES_NAME = 'fitted.values'
- CONVERGENCE_NAME = 'convergence'
-
- @property
- def deviance(self):
- return np.array(self.name_to_value['deviance'])[0]
-
- @property
- def convergence(self) -> str:
- convergence_value = self.name_to_value[self.CONVERGENCE_NAME]
- return convergence_value[0]
-
- @property
- def is_convergence_successful(self) -> bool:
- return self.convergence == "successful"
-
- @property
- def all_parameters(self) -> Dict[str, float]:
- fitted_values = self.name_to_value[self.FITTED_VALUES_NAME]
- return {key: fitted_values.rx2(key)[0] for key in fitted_values.names}
-
- @property
- def margin_coef_dict(self):
- return {k: v for k, v in self.all_parameters.items() if LinearCoef.COEFF_STR in k}
-
- @property
- def other_coef_dict(self):
- return {k: v for k, v in self.all_parameters.items() if LinearCoef.COEFF_STR not in k}
diff --git a/extreme_estimator/extreme_models/result_from_model_fit/__init__.py b/extreme_estimator/extreme_models/result_from_model_fit/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/extreme_estimator/extreme_models/result_from_model_fit/abstract_result_from_model_fit.py b/extreme_estimator/extreme_models/result_from_model_fit/abstract_result_from_model_fit.py
new file mode 100644
index 00000000..5560e1c8
--- /dev/null
+++ b/extreme_estimator/extreme_models/result_from_model_fit/abstract_result_from_model_fit.py
@@ -0,0 +1,45 @@
+import numpy as np
+from rpy2 import robjects
+
+
+
+class AbstractResultFromModelFit(object):
+
+ def __init__(self, result_from_fit: robjects.ListVector) -> None:
+ if hasattr(result_from_fit, 'names'):
+ self.name_to_value = {name: result_from_fit.rx2(name) for name in result_from_fit.names}
+ else:
+ self.name_to_value = {}
+
+ @property
+ def names(self):
+ return self.name_to_value.keys()
+
+ @property
+ def all_parameters(self):
+ raise NotImplementedError
+
+ @property
+ def margin_coef_dict(self):
+ raise NotImplementedError
+
+ @property
+ def other_coef_dict(self):
+ raise NotImplementedError
+
+ @property
+ def nllh(self):
+ raise NotImplementedError
+
+ @property
+ def deviance(self):
+ raise NotImplementedError
+
+ @property
+ def convergence(self) -> str:
+ raise NotImplementedError
+
+
+
+
+
diff --git a/extreme_estimator/extreme_models/result_from_model_fit/result_from_extremes.py b/extreme_estimator/extreme_models/result_from_model_fit/result_from_extremes.py
new file mode 100644
index 00000000..0103b8be
--- /dev/null
+++ b/extreme_estimator/extreme_models/result_from_model_fit/result_from_extremes.py
@@ -0,0 +1,52 @@
+from rpy2 import robjects
+
+from extreme_estimator.extreme_models.result_from_model_fit.abstract_result_from_model_fit import \
+ AbstractResultFromModelFit
+
+
+class ResultFromExtremes(AbstractResultFromModelFit):
+
+ def __init__(self, result_from_fit: robjects.ListVector, gev_param_name_to_dim=None) -> None:
+ super().__init__(result_from_fit)
+ self.gev_param_name_to_dim = gev_param_name_to_dim
+ print(list(self.name_to_value.keys()))
+
+ # @property
+ # def
+
+ # @property
+ # def margin_coef_dict(self):
+ # assert self.gev_param_name_to_dim is not None
+ # # Build the Coeff dict from gev_param_name_to_dim
+ # coef_dict = {}
+ # i = 0
+ # mle_values = self.name_to_value['mle']
+ # for gev_param_name in GevParams.PARAM_NAMES:
+ # # Add intercept
+ # intercept_coef_name = LinearCoef.coef_template_str(gev_param_name, LinearCoef.INTERCEPT_NAME).format(1)
+ # coef_dict[intercept_coef_name] = mle_values[i]
+ # i += 1
+ # # Add a potential linear temporal trend
+ # if gev_param_name in self.gev_param_name_to_dim:
+ # temporal_coef_name = LinearCoef.coef_template_str(gev_param_name,
+ # AbstractCoordinates.COORDINATE_T).format(1)
+ # coef_dict[temporal_coef_name] = mle_values[i]
+ # i += 1
+ # return coef_dict
+
+ # @property
+ # def all_parameters(self):
+ # return self.margin_coef_dict
+ #
+ # @property
+ # def nllh(self):
+ # return convertFloatVector_to_float(self.name_to_value['nllh'])
+ #
+ # @property
+ # def deviance(self):
+ # return - 2 * self.nllh
+ #
+ # @property
+ # def convergence(self) -> str:
+ # return convertFloatVector_to_float(self.name_to_value['conv']) == 0
+
diff --git a/extreme_estimator/extreme_models/result_from_model_fit/result_from_ismev.py b/extreme_estimator/extreme_models/result_from_model_fit/result_from_ismev.py
new file mode 100644
index 00000000..0300d86e
--- /dev/null
+++ b/extreme_estimator/extreme_models/result_from_model_fit/result_from_ismev.py
@@ -0,0 +1,54 @@
+from typing import Dict
+
+import numpy as np
+from rpy2 import robjects
+
+from extreme_estimator.extreme_models.margin_model.param_function.linear_coef import LinearCoef
+from extreme_estimator.extreme_models.result_from_model_fit.abstract_result_from_model_fit import \
+ AbstractResultFromModelFit
+from extreme_estimator.extreme_models.result_from_model_fit.utils import convertFloatVector_to_float
+from extreme_estimator.margin_fits.gev.gev_params import GevParams
+from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+
+
+class ResultFromIsmev(AbstractResultFromModelFit):
+
+ def __init__(self, result_from_fit: robjects.ListVector, gev_param_name_to_dim=None) -> None:
+ super().__init__(result_from_fit)
+ self.gev_param_name_to_dim = gev_param_name_to_dim
+
+ @property
+ def margin_coef_dict(self):
+ assert self.gev_param_name_to_dim is not None
+ # Build the Coeff dict from gev_param_name_to_dim
+ coef_dict = {}
+ i = 0
+ mle_values = self.name_to_value['mle']
+ for gev_param_name in GevParams.PARAM_NAMES:
+ # Add intercept
+ intercept_coef_name = LinearCoef.coef_template_str(gev_param_name, LinearCoef.INTERCEPT_NAME).format(1)
+ coef_dict[intercept_coef_name] = mle_values[i]
+ i += 1
+ # Add a potential linear temporal trend
+ if gev_param_name in self.gev_param_name_to_dim:
+ temporal_coef_name = LinearCoef.coef_template_str(gev_param_name,
+ AbstractCoordinates.COORDINATE_T).format(1)
+ coef_dict[temporal_coef_name] = mle_values[i]
+ i += 1
+ return coef_dict
+
+ @property
+ def all_parameters(self):
+ return self.margin_coef_dict
+
+ @property
+ def nllh(self):
+ return convertFloatVector_to_float(self.name_to_value['nllh'])
+
+ @property
+ def deviance(self):
+ return - 2 * self.nllh
+
+ @property
+ def convergence(self) -> str:
+ return convertFloatVector_to_float(self.name_to_value['conv']) == 0
diff --git a/extreme_estimator/extreme_models/result_from_model_fit/result_from_spatial_extreme.py b/extreme_estimator/extreme_models/result_from_model_fit/result_from_spatial_extreme.py
new file mode 100644
index 00000000..bf98c665
--- /dev/null
+++ b/extreme_estimator/extreme_models/result_from_model_fit/result_from_spatial_extreme.py
@@ -0,0 +1,41 @@
+from typing import Dict
+
+import numpy as np
+
+from extreme_estimator.extreme_models.margin_model.param_function.linear_coef import LinearCoef
+from extreme_estimator.extreme_models.result_from_model_fit.abstract_result_from_model_fit import \
+ AbstractResultFromModelFit
+
+
+class ResultFromSpatialExtreme(AbstractResultFromModelFit):
+ """
+ Handler from any result with the result of a fit functions from the package Spatial Extreme
+ """
+ FITTED_VALUES_NAME = 'fitted.values'
+ CONVERGENCE_NAME = 'convergence'
+
+ @property
+ def deviance(self):
+ return np.array(self.name_to_value['deviance'])[0]
+
+ @property
+ def convergence(self) -> str:
+ convergence_value = self.name_to_value[self.CONVERGENCE_NAME]
+ return convergence_value[0]
+
+ @property
+ def is_convergence_successful(self) -> bool:
+ return self.convergence == "successful"
+
+ @property
+ def all_parameters(self) -> Dict[str, float]:
+ fitted_values = self.name_to_value[self.FITTED_VALUES_NAME]
+ return {key: fitted_values.rx2(key)[0] for key in fitted_values.names}
+
+ @property
+ def margin_coef_dict(self):
+ return {k: v for k, v in self.all_parameters.items() if LinearCoef.COEFF_STR in k}
+
+ @property
+ def other_coef_dict(self):
+ return {k: v for k, v in self.all_parameters.items() if LinearCoef.COEFF_STR not in k}
diff --git a/extreme_estimator/extreme_models/result_from_model_fit/utils.py b/extreme_estimator/extreme_models/result_from_model_fit/utils.py
new file mode 100644
index 00000000..ebd20e93
--- /dev/null
+++ b/extreme_estimator/extreme_models/result_from_model_fit/utils.py
@@ -0,0 +1,5 @@
+import numpy as np
+
+
+def convertFloatVector_to_float(f):
+ return np.array(f)[0]
diff --git a/extreme_estimator/extreme_models/utils.py b/extreme_estimator/extreme_models/utils.py
index c2fe6ea0..d085837c 100644
--- a/extreme_estimator/extreme_models/utils.py
+++ b/extreme_estimator/extreme_models/utils.py
@@ -18,13 +18,23 @@ from rpy2.rinterface._rinterface import RRuntimeError
from rpy2.robjects import numpy2ri
from rpy2.robjects import pandas2ri
+from utils import get_root_path
+
+# Load R variables
r = ro.R()
numpy2ri.activate()
pandas2ri.activate()
r.library('SpatialExtremes')
+# Desactivate temporarily warnings
default_filters = warnings.filters.copy()
warnings.filterwarnings("ignore")
+# Load ismev
r.library('ismev')
+# Load fevd fixed
+fevd_fixed_filepath = op.join(get_root_path(), 'extreme_estimator', 'margin_fits', 'gev', 'fevd_fixed.R')
+assert op.exists(fevd_fixed_filepath)
+r.source(fevd_fixed_filepath)
+# Reactivate warning
warnings.filters = default_filters
diff --git a/extreme_estimator/margin_fits/margin_fits_utils.py b/extreme_estimator/margin_fits/margin_fits_utils.py
index bc41ff95..182ff557 100644
--- a/extreme_estimator/margin_fits/margin_fits_utils.py
+++ b/extreme_estimator/margin_fits/margin_fits_utils.py
@@ -1,8 +1,6 @@
-import numpy as np
import rpy2.robjects as ro
-from extreme_estimator.extreme_models.result_from_fit import ResultFromIsmev
-from extreme_estimator.extreme_models.utils import r, get_null, safe_run_r_estimator
+from extreme_estimator.extreme_models.utils import r, get_null
"""
These two functions are “extremely light†functions to fit the GEV/GPD. These functions are mainlyuseful
diff --git a/test/test_experiment/test_coordinate_sensitivity.py b/test/test_experiment/test_coordinate_sensitivity.py
index f6178e13..081d5ce0 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_fitted.coordinates.df_all_coordinates)
- print(stationary_est.result_from_fit.convergence)
- print(stationary_est.margin_function_fitted.coef_dict)
+ print(stationary_est.margin_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('Non Stationary')
non_stationary_est = trend_test.get_estimator(starting_point=1960)
- print(non_stationary_est.result_from_fit.convergence)
+ 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_fit.convergence)
- print(non_stationary_est.margin_function_fitted.coef_dict)
+ print(non_stationary_est.result_from_model_fit.convergence)
+ print(non_stationary_est.margin_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_estimator/test_estimator/test_margin_estimators.py b/test/test_extreme_estimator/test_estimator/test_margin_estimators.py
index 11f2813f..7d6ac6a6 100644
--- a/test/test_extreme_estimator/test_estimator/test_margin_estimators.py
+++ b/test/test_extreme_estimator/test_estimator/test_margin_estimators.py
@@ -30,11 +30,11 @@ class TestSmoothMarginEstimator(unittest.TestCase):
# Fit estimator
estimator = LinearMarginEstimator(dataset=dataset, margin_model=margin_model)
estimator.fit()
- print(estimator.result_from_fit.name_to_value.keys())
+ print(estimator.result_from_model_fit.name_to_value.keys())
# Plot
if self.DISPLAY:
margin_model.margin_function_sample.visualize_function(show=True)
- estimator.margin_function_fitted.visualize_function(show=True)
+ estimator.margin_function_from_fit.visualize_function(show=True)
self.assertTrue(True)
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 affe7ad8..7e2c5f6e 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
@@ -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_fitted.get_gev_params(coordinate).to_dict()
+ mle_params_estimated = estimator.margin_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_fitted.mu1_temporal_trend, 0.0)
+ self.assertNotEqual(estimator.margin_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_fitted.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.margin_function_from_fit.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()
+ mle_params_estimated_year3 = estimator.margin_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_fitted.mu1_temporal_trend, 0.0)
- self.assertAlmostEqual(estimator.margin_function_fitted.mu1_temporal_trend,
+ self.assertNotEqual(estimator.margin_function_from_fit.mu1_temporal_trend, 0.0)
+ self.assertAlmostEqual(estimator.margin_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_fitted.starting_point)
+ self.assertEqual(2, estimator.margin_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_fitted.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.margin_function_from_fit.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()
+ mle_params_estimated_year2 = estimator.margin_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_fitted.get_gev_params(coordinate5).to_dict()
+ mle_params_estimated_year5 = estimator.margin_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_fitted.mu1_temporal_trend
- mu1_estimator2 = estimator2.margin_function_fitted.mu1_temporal_trend
+ mu1_estimator1 = estimator1.margin_function_from_fit.mu1_temporal_trend
+ mu1_estimator2 = estimator2.margin_function_from_fit.mu1_temporal_trend
self.assertNotEqual(mu1_estimator1, mu1_estimator2)
diff --git a/test/test_extreme_estimator/test_extreme_models/test_margin_temporal_transformed.py b/test/test_extreme_estimator/test_extreme_models/test_margin_temporal_transformed.py
index d3a9ca08..975c7f28 100644
--- a/test/test_extreme_estimator/test_extreme_models/test_margin_temporal_transformed.py
+++ b/test/test_extreme_estimator/test_extreme_models/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_fitted.get_gev_params(coordinate,
- is_transformed=False).to_dict()
+ mle_params_estimated = estimator.margin_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_fitted.mu1_temporal_trend, 0.0)
+ self.assertNotEqual(estimator.margin_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_fitted.get_gev_params(coordinate1,
- is_transformed=False).to_dict()
+ mle_params_estimated_year1 = estimator.margin_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_fitted.get_gev_params(coordinate3,
- is_transformed=False).to_dict()
+ mle_params_estimated_year3 = estimator.margin_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_fitted.mu1_temporal_trend, 0.0)
+ self.assertNotEqual(estimator.margin_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_fitted.get_gev_params(coordinate1,
- is_transformed=False).to_dict()
+ mle_params_estimated_year1 = estimator.margin_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_fitted.get_gev_params(coordinate2,
- is_transformed=False).to_dict()
+ mle_params_estimated_year2 = estimator.margin_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_fitted.get_gev_params(coordinate5,
- is_transformed=False).to_dict()
+ mle_params_estimated_year5 = estimator.margin_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_fitted.mu1_temporal_trend
- mu1_estimator2 = estimator2.margin_function_fitted.mu1_temporal_trend
+ mu1_estimator1 = estimator1.margin_function_from_fit.mu1_temporal_trend
+ mu1_estimator2 = estimator2.margin_function_from_fit.mu1_temporal_trend
self.assertNotEqual(mu1_estimator1, mu1_estimator2)
diff --git a/test/test_extreme_estimator/test_extreme_models/test_max_stable_temporal.py b/test/test_extreme_estimator/test_extreme_models/test_max_stable_temporal.py
index c48667bd..07570582 100644
--- a/test/test_extreme_estimator/test_extreme_models/test_max_stable_temporal.py
+++ b/test/test_extreme_estimator/test_extreme_models/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_fitted.get_gev_params(coordinate).to_dict()
+ mle_params_estimated = estimator.margin_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_fitted.mu1_temporal_trend, 0.0)
+ self.assertNotEqual(estimator.margin_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_fitted.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.margin_function_from_fit.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()
+ mle_params_estimated_year3 = estimator.margin_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_fitted.mu1_temporal_trend, 0.0)
- self.assertAlmostEqual(estimator.margin_function_fitted.mu1_temporal_trend,
+ self.assertNotEqual(estimator.margin_function_from_fit.mu1_temporal_trend, 0.0)
+ self.assertAlmostEqual(estimator.margin_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_fitted.starting_point)
+ self.assertEqual(2, estimator.margin_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_fitted.get_gev_params(coordinate1).to_dict()
+ mle_params_estimated_year1 = estimator.margin_function_from_fit.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()
+ mle_params_estimated_year2 = estimator.margin_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_fitted.get_gev_params(coordinate5).to_dict()
+ mle_params_estimated_year5 = estimator.margin_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_fitted.mu1_temporal_trend
- mu1_estimator2 = estimator2.margin_function_fitted.mu1_temporal_trend
+ mu1_estimator1 = estimator1.margin_function_from_fit.mu1_temporal_trend
+ mu1_estimator2 = estimator2.margin_function_from_fit.mu1_temporal_trend
self.assertNotEqual(mu1_estimator1, mu1_estimator2)
diff --git a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py
index 64403ba0..3b7113fa 100644
--- a/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py
+++ b/test/test_extreme_estimator/test_margin_fits/test_gev/test_gev_temporal.py
@@ -41,7 +41,7 @@ class TestGevTemporal(unittest.TestCase):
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()
+ mle_params_estimated = estimator.margin_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)
@@ -53,21 +53,21 @@ 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_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()
+ 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()
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)
+ self.assertNotEqual(estimator.margin_function_from_fit.mu1_temporal_trend, 0.0)
# Checks starting point parameter are well passed
- self.assertEqual(3, estimator.margin_function_fitted.starting_point)
+ self.assertEqual(3, estimator.margin_function_from_fit.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()
+ 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()
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()
+ mle_params_estimated_year5 = estimator.margin_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):
@@ -80,8 +80,8 @@ 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_fitted.mu1_temporal_trend
- mu1_estimator2 = estimator2.margin_function_fitted.mu1_temporal_trend
+ mu1_estimator1 = estimator1.margin_function_from_fit.mu1_temporal_trend
+ mu1_estimator2 = estimator2.margin_function_from_fit.mu1_temporal_trend
self.assertNotEqual(mu1_estimator1, mu1_estimator2)
diff --git a/test/test_unitary/test_fitmaxstab/test_fitmaxstab_with_margin.py b/test/test_unitary/test_fitmaxstab/test_fitmaxstab_with_margin.py
index 86d74df4..c4c16052 100644
--- a/test/test_unitary/test_fitmaxstab/test_fitmaxstab_with_margin.py
+++ b/test/test_unitary/test_fitmaxstab/test_fitmaxstab_with_margin.py
@@ -32,7 +32,7 @@ class TestMaxStableFitWithConstantMargin(TestUnitaryAbstract):
full_estimator = FullEstimatorInASingleStepWithSmoothMargin(dataset, margin_model,
max_stable_model)
full_estimator.fit()
- return full_estimator.result_from_fit.all_parameters
+ return full_estimator.result_from_model_fit.all_parameters
def test_max_stable_fit_with_constant_margin(self):
self.compare()
@@ -59,7 +59,7 @@ class TestMaxStableFitWithLinearMargin(TestUnitaryAbstract):
full_estimator = FullEstimatorInASingleStepWithSmoothMargin(dataset, margin_model,
max_stable_model)
full_estimator.fit()
- return full_estimator.result_from_fit.all_parameters
+ return full_estimator.result_from_model_fit.all_parameters
def test_max_stable_fit_with_linear_margin(self):
self.compare()
--
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