diff --git a/extreme_fit/estimator/full_estimator/abstract_full_estimator.py b/extreme_fit/estimator/full_estimator/abstract_full_estimator.py
index b6ff59ce572d0c4c3200296cd5f00dc5d0ed4061..022bb4d3947fae1785989f14a26de0400db43421 100644
--- a/extreme_fit/estimator/full_estimator/abstract_full_estimator.py
+++ b/extreme_fit/estimator/full_estimator/abstract_full_estimator.py
@@ -59,11 +59,11 @@ class FullEstimatorInASingleStepWithSmoothMargin(AbstractFullEstimator):
super().__init__(dataset)
self.max_stable_model = max_stable_model
self.linear_margin_model = margin_model
- assert isinstance(self.margin_function_start_fit, LinearMarginFunction)
+ assert isinstance(self.margin_function, LinearMarginFunction)
@property
- def margin_function_start_fit(self):
- return self.linear_margin_model.margin_function_start_fit
+ def margin_function(self):
+ return self.linear_margin_model.margin_function
@property
def df_coordinates_spat(self):
@@ -81,8 +81,8 @@ class FullEstimatorInASingleStepWithSmoothMargin(AbstractFullEstimator):
df_coordinates_spat=self.df_coordinates_spat,
df_coordinates_temp=self.df_coordinates_temp,
fit_marge=True,
- fit_marge_form_dict=self.linear_margin_model.margin_function_start_fit.form_dict,
- margin_start_dict=self.linear_margin_model.margin_function_start_fit.coef_dict
+ fit_marge_form_dict=self.linear_margin_model.margin_function.form_dict,
+ margin_start_dict=self.linear_margin_model.margin_function.coef_dict
)
@cached_property
diff --git a/extreme_fit/estimator/utils.py b/extreme_fit/estimator/utils.py
index a7446b45bd83f2d1aae856cdd0fefde1a816ebe2..d8b5e97e1af29e3353212ad0bdc615549e5b3fbb 100644
--- a/extreme_fit/estimator/utils.py
+++ b/extreme_fit/estimator/utils.py
@@ -10,7 +10,7 @@ def load_margin_function(estimator: AbstractEstimator, margin_model: LinearMargi
if coef_dict is None:
coef_dict = estimator.result_from_model_fit.margin_coef_ordered_dict
return margin_function_class.from_coef_dict(coordinates=estimator.dataset.coordinates,
- param_name_to_dims=margin_model.margin_function_start_fit.param_name_to_dims,
+ param_name_to_dims=margin_model.margin_function.param_name_to_dims,
coef_dict=coef_dict,
starting_point=margin_model.starting_point)
diff --git a/extreme_fit/model/abstract_model.py b/extreme_fit/model/abstract_model.py
index 71fe7319db2cd738920f0e4ef626d3ff80f422aa..331d275365931a3604c8889057b4616b785df772 100644
--- a/extreme_fit/model/abstract_model.py
+++ b/extreme_fit/model/abstract_model.py
@@ -4,11 +4,6 @@ class AbstractModel(object):
self.default_params = None
self.user_params_sample = params_sample
- @property
- def params_start_fit(self) -> dict:
- # return self.default_params.copy()
- return self.params_sample.copy()
-
@property
def params_sample(self) -> dict:
return self.merge_params(default_params=self.default_params, input_params=self.user_params_sample)
diff --git a/extreme_fit/model/margin_model/abstract_margin_model.py b/extreme_fit/model/margin_model/abstract_margin_model.py
index f3129472da53c7d81a98d07ffb17db0febe73470..8358b41edb49334559e7f252e61946e3c76d8639 100644
--- a/extreme_fit/model/margin_model/abstract_margin_model.py
+++ b/extreme_fit/model/margin_model/abstract_margin_model.py
@@ -14,9 +14,8 @@ from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoo
class AbstractMarginModel(AbstractModel, ABC):
"""
- An AbstractMarginModel has two main AbstractMarginFunction attributes:
- -margin_function_sample for sampling
- -margin_function_start_fit for starting to fit
+ An AbstractMarginModel has one AbstractMarginFunction attribute:
+ -margin_function
"""
def __init__(self, coordinates: AbstractCoordinates, params_sample=None,
@@ -24,8 +23,8 @@ class AbstractMarginModel(AbstractModel, ABC):
super().__init__(params_sample)
assert isinstance(coordinates, AbstractCoordinates), type(coordinates)
self.coordinates = coordinates
- self.margin_function_sample = None # type: AbstractMarginFunction
- self.margin_function_start_fit = None # type: AbstractMarginFunction
+ self.margin_function = None # type: AbstractMarginFunction
+ self.margin_function = None # type: AbstractMarginFunction
self.params_class = params_class
self.load_margin_functions()
@@ -34,11 +33,8 @@ class AbstractMarginModel(AbstractModel, ABC):
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=self.params_class.from_dict(self.params_sample))
- self.margin_function_start_fit = margin_function_class(coordinates=self.coordinates,
- default_params=self.params_class.from_dict(
- self.params_start_fit))
+ self.margin_function = margin_function_class(coordinates=self.coordinates,
+ default_params=self.params_class.from_dict(self.params_sample))
# Conversion class methods
@@ -67,14 +63,14 @@ class AbstractMarginModel(AbstractModel, ABC):
# Sampling methods
def rmargin_from_maxima_frech(self, maxima_frech: np.ndarray, coordinates_values: np.ndarray) -> np.ndarray:
- maxima_gev = self.frech2gev(maxima_frech, coordinates_values, self.margin_function_sample)
+ maxima_gev = self.frech2gev(maxima_frech, coordinates_values, self.margin_function)
return maxima_gev
def rmargin_from_nb_obs(self, nb_obs: int, coordinates_values: np.ndarray,
sample_r_function='rgev') -> np.ndarray:
maxima_gev = []
for coordinate in coordinates_values:
- gev_params = self.margin_function_sample.get_params(coordinate)
+ gev_params = self.margin_function.get_params(coordinate)
x_gev = r(sample_r_function)(nb_obs, **gev_params.to_dict())
assert not np.isnan(x_gev).any(), 'params={} generated Nan values'.format(gev_params.__str__())
maxima_gev.append(x_gev)
diff --git a/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py b/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
index 1005c89de4ea3fb94b5b633c87bd6809a488c22f..5e1d147b8fa6a899c2d25666fad6c7351c61d27c 100644
--- a/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
+++ b/extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py
@@ -60,7 +60,7 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
res = safe_run_r_estimator(function=r('gev.fit'),
xdat=x, y=y, mul=self.mul,
sigl=self.sigl, shl=self.shl)
- return ResultFromIsmev(res, self.margin_function_start_fit.param_name_to_dims)
+ return ResultFromIsmev(res, self.margin_function.param_name_to_dims)
# Gev fit with extRemes package
@@ -77,7 +77,7 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
def run_fevd_fixed(self, df_coordinates_temp, method, x):
if self.fit_method == MarginFitMethod.extremes_fevd_l_moments:
- assert self.margin_function_start_fit.is_a_stationary_model
+ assert self.margin_function.is_a_stationary_model
r_type_argument_kwargs, y = self.extreme_arguments(df_coordinates_temp)
res = safe_run_r_estimator(function=r('fevd_fixed'),
x=x,
@@ -86,7 +86,7 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
method=method,
**r_type_argument_kwargs
)
- return ResultFromMleExtremes(res, self.margin_function_start_fit.param_name_to_dims,
+ return ResultFromMleExtremes(res, self.margin_function.param_name_to_dims,
type_for_mle=self.type_for_mle)
def extremes_fevd_bayesian_fit(self, x, df_coordinates_temp) -> AbstractResultFromExtremes:
@@ -95,8 +95,8 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
r_type_argument_kwargs['initial'] = r.list(**params_initial_fit)
# Assert for any non-stationary model that the shape parameter is constant
# (because the prior function considers that the last parameter should be the shape)
- assert GevParams.SHAPE not in self.margin_function_start_fit.param_name_to_dims \
- or len(self.margin_function_start_fit.param_name_to_dims[GevParams.SHAPE]) == 1
+ assert GevParams.SHAPE not in self.margin_function.param_name_to_dims \
+ or len(self.margin_function.param_name_to_dims[GevParams.SHAPE]) == 1
res = safe_run_r_estimator(function=r('fevd_fixed'),
x=x,
data=y,
@@ -106,14 +106,14 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
iter=self.nb_iterations_for_bayesian_fit,
**r_type_argument_kwargs
)
- return ResultFromBayesianExtremes(res, self.margin_function_start_fit.param_name_to_dims)
+ return ResultFromBayesianExtremes(res, self.margin_function.param_name_to_dims)
def extreme_arguments(self, df_coordinates_temp):
# Disable the use of log sigma parametrization
r_type_argument_kwargs = {'use.phi': False,
'verbose': False}
# Load parameters
- r_type_argument_kwargs.update(get_margin_formula_extremes(self.margin_function_start_fit.form_dict))
+ r_type_argument_kwargs.update(get_margin_formula_extremes(self.margin_function.form_dict))
y = get_coord_df(df_coordinates_temp)
return r_type_argument_kwargs, y
diff --git a/extreme_fit/model/margin_model/linear_margin_model/linear_margin_model.py b/extreme_fit/model/margin_model/linear_margin_model/linear_margin_model.py
index 23ecd372f38e214a152eaadd55d3437453f5a618..f6091c29514a851159dea019cf26b8700c7ab69b 100644
--- a/extreme_fit/model/margin_model/linear_margin_model/linear_margin_model.py
+++ b/extreme_fit/model/margin_model/linear_margin_model/linear_margin_model.py
@@ -23,19 +23,11 @@ class LinearMarginModel(ParametricMarginModel):
# Load sample coef
coef_sample = self.param_name_to_linear_coef(param_name_and_dim_to_coef=self.params_sample)
- self.margin_function_sample = LinearMarginFunction(coordinates=self.coordinates,
- param_name_to_coef=coef_sample,
- param_name_to_dims=param_name_to_dims,
- starting_point=self.starting_point,
- params_class=self.params_class)
-
- # Load start fit coef
- coef_start_fit = self.param_name_to_linear_coef(param_name_and_dim_to_coef=self.params_start_fit)
- self.margin_function_start_fit = LinearMarginFunction(coordinates=self.coordinates,
- param_name_to_coef=coef_start_fit,
- param_name_to_dims=param_name_to_dims,
- starting_point=self.starting_point,
- params_class=self.params_class)
+ self.margin_function = LinearMarginFunction(coordinates=self.coordinates,
+ param_name_to_coef=coef_sample,
+ param_name_to_dims=param_name_to_dims,
+ starting_point=self.starting_point,
+ params_class=self.params_class)
@property
def default_param_name_and_dim_to_coef(self) -> dict:
diff --git a/extreme_fit/model/margin_model/parametric_margin_model.py b/extreme_fit/model/margin_model/parametric_margin_model.py
index e9af27077a3b7bf2f4677d55d05061da0ff4cf03..3e638248a11397b69e498b3e9b7944a530b58ca9 100644
--- a/extreme_fit/model/margin_model/parametric_margin_model.py
+++ b/extreme_fit/model/margin_model/parametric_margin_model.py
@@ -24,8 +24,7 @@ class ParametricMarginModel(AbstractMarginModel, ABC):
"""
self.fit_method = fit_method
self.starting_point = starting_point
- self.margin_function_sample = None # type: ParametricMarginFunction
- self.margin_function_start_fit = None # type: ParametricMarginFunction
+ self.margin_function = None # type: ParametricMarginFunction
self.drop_duplicates = True
super().__init__(coordinates, params_sample, params_class)
@@ -39,12 +38,12 @@ class ParametricMarginModel(AbstractMarginModel, ABC):
def fit_from_spatial_extremes(self, data, df_coordinates_spat, df_coordinates_temp):
# Margin formula for fitspatgev
- fit_params = get_margin_formula_spatial_extreme(self.margin_function_start_fit.form_dict)
+ fit_params = get_margin_formula_spatial_extreme(self.margin_function.form_dict)
# Covariables
covariables = get_coord(df_coordinates=df_coordinates_spat)
fit_params['temp.cov'] = get_coord(df_coordinates=df_coordinates_temp)
# Start parameters
- coef_dict = self.margin_function_start_fit.coef_dict
+ coef_dict = self.margin_function.coef_dict
# fit_params['start'] = r.list(**coef_dict)
res = safe_run_r_estimator(function=r.fitspatgev, data=data,
start_dict=coef_dict,
diff --git a/extreme_fit/model/margin_model/spline_margin_model.py b/extreme_fit/model/margin_model/spline_margin_model.py
index cfe0e1b04a3475579c53d77a7c0c0548dfb48867..f9fec6a4209463bb63624e38a10d3f10f08ac7b3 100644
--- a/extreme_fit/model/margin_model/spline_margin_model.py
+++ b/extreme_fit/model/margin_model/spline_margin_model.py
@@ -37,17 +37,11 @@ class SplineMarginModel(ParametricMarginModel):
param_name_to_nb_knots = {param_name: 2 for param_name in GevParams.PARAM_NAMES}
# Load sample coef
- self.margin_function_sample = SplineMarginFunction(coordinates=self.coordinates,
- param_name_to_dims=param_name_to_dims,
- param_name_to_coef=param_name_to_coef,
- param_name_to_nb_knots=param_name_to_nb_knots,
- degree=degree)
- # Load start fit coef
- self.margin_function_start_fit = SplineMarginFunction(coordinates=self.coordinates,
- param_name_to_dims=param_name_to_dims,
- param_name_to_coef=param_name_to_coef,
- param_name_to_nb_knots=param_name_to_nb_knots,
- degree=degree)
+ self.margin_function = SplineMarginFunction(coordinates=self.coordinates,
+ param_name_to_dims=param_name_to_dims,
+ param_name_to_coef=param_name_to_coef,
+ param_name_to_nb_knots=param_name_to_nb_knots,
+ degree=degree)
class ConstantSplineMarginModel(SplineMarginModel):
diff --git a/extreme_fit/model/max_stable_model/abstract_max_stable_model.py b/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
index 40634790f32cae5c0d16b8498dca4419cf8abf29..8e7cb652f55d7ce08b80f59189527813b1e5216d 100644
--- a/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
+++ b/extreme_fit/model/max_stable_model/abstract_max_stable_model.py
@@ -49,7 +49,7 @@ class AbstractMaxStableModel(AbstractModel):
# Prepare the fit_params (a dictionary containing all additional parameters)
fit_params = self.cov_mod_param.copy()
- start_dict = self.params_start_fit
+ start_dict = self.params_sample
# Remove some parameters that should only be used either in 1D or 2D case, otherwise fitmaxstab crashes
start_dict = self.remove_unused_parameters(start_dict, fitmaxstab_with_one_dimensional_data)
if fit_marge:
diff --git a/projects/quantile_regression_vs_evt/annual_maxima_simulation/abstract_annual_maxima_simulation.py b/projects/quantile_regression_vs_evt/annual_maxima_simulation/abstract_annual_maxima_simulation.py
index 29bfb2b7dc2c1184764f10c9772b1fe3b4fb88b0..1ba0cc19aa478ebb353ce01d83e717c480269740 100644
--- a/projects/quantile_regression_vs_evt/annual_maxima_simulation/abstract_annual_maxima_simulation.py
+++ b/projects/quantile_regression_vs_evt/annual_maxima_simulation/abstract_annual_maxima_simulation.py
@@ -47,7 +47,7 @@ class AnnualMaximaSimulation(AbstractSimulation):
last_coordinate = coordinates.coordinates_values()[-1]
# Compute true value
margin_model = self.time_series_lengths_to_margin_model[length]
- true_gev_params = margin_model.margin_function_sample.get_params(last_coordinate)
+ true_gev_params = margin_model.margin_function.get_params(last_coordinate)
true_quantile = true_gev_params.quantile(self.quantile_data)
# Compute estimated values
estimated_quantiles = [estimator.function_from_fit.get_quantile(last_coordinate) for estimator in estimators]
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 9a5a5c552fe13e38d25c7cb7017facd2787bacca..2d998f99c5df07b82f29f34d23d3cc06c7f5eccf 100644
--- a/test/test_extreme_fit/test_estimator/test_margin_estimators.py
+++ b/test/test_extreme_fit/test_estimator/test_margin_estimators.py
@@ -33,7 +33,7 @@ class TestSmoothMarginEstimator(unittest.TestCase):
print(estimator.result_from_model_fit.name_to_value.keys())
# Plot
if self.DISPLAY:
- margin_model.margin_function_sample.visualize_function(show=True)
+ margin_model.margin_function.visualize_function(show=True)
estimator.function_from_fit.visualize_function(show=True)
self.assertTrue(True)
diff --git a/test/test_extreme_fit/test_function/test_margin_function.py b/test/test_extreme_fit/test_function/test_margin_function.py
index 10f83b7ba76a3a95f2884c18d6299fba6a8324f9..25f4443766727bbe008fdcf468fc4f1cff295f80 100644
--- a/test/test_extreme_fit/test_function/test_margin_function.py
+++ b/test/test_extreme_fit/test_function/test_margin_function.py
@@ -30,7 +30,7 @@ class MarginFunction(unittest.TestCase):
'tempCoeffLoc1': 1, 'tempCoeffScale1': 1,
'tempCoeffShape1': 1}
margin_function = self.margin_function_class.from_coef_dict(coordinates,
- margin_model.margin_function_sample.param_name_to_dims,
+ margin_model.margin_function.param_name_to_dims,
coef_dict)
gev_param = margin_function.get_params(coordinate=np.array([0.5, 1.0]), is_transformed=False)
self.assertEqual({'loc': 2, 'scale': 2, 'shape': 2}, gev_param.to_dict())
@@ -44,7 +44,7 @@ class MarginFunction(unittest.TestCase):
'scaleCoeff2': 1, 'shapeCoeff1': 0,
'shapeCoeff2': 1}
margin_function = self.margin_function_class.from_coef_dict(coordinates,
- margin_model.margin_function_sample.param_name_to_dims,
+ margin_model.margin_function.param_name_to_dims,
coef_dict)
gev_param = margin_function.get_params(coordinate=np.array([1]), is_transformed=False)
self.assertEqual({'loc': 3, 'scale': 1, 'shape': 1}, gev_param.to_dict())
diff --git a/test/test_extreme_fit/test_model/test_margin_model.py b/test/test_extreme_fit/test_model/test_margin_model.py
index 5bae40a6d793a3c17977ebe6f3b51218a6ac9bf8..626a624b3ff4ca9fd9d71fc612d60c5086c3accf 100644
--- a/test/test_extreme_fit/test_model/test_margin_model.py
+++ b/test/test_extreme_fit/test_model/test_margin_model.py
@@ -14,7 +14,7 @@ class TestVisualizationLinearMarginModel(unittest.TestCase):
margin_model_class = LinearAllParametersAllDimsMarginModel
def tearDown(self) -> None:
- self.margin_model.margin_function_sample.visualize_function(show=self.DISPLAY)
+ self.margin_model.margin_function.visualize_function(show=self.DISPLAY)
self.assertTrue(True)
def test_example_visualization_1D(self):
@@ -37,7 +37,7 @@ class TestVisualizationSplineMarginModel(unittest.TestCase):
margin_model_class = Degree1SplineMarginModel
def tearDown(self) -> None:
- self.margin_model.margin_function_sample.visualize_function(show=self.DISPLAY)
+ self.margin_model.margin_function.visualize_function(show=self.DISPLAY)
self.assertTrue(True)
def test_example_visualization_1D_spline(self):
@@ -50,7 +50,7 @@ class TestVisualizationSplineMarginModel(unittest.TestCase):
# TODO: add a similar test than in the linear case
# # Assert that the grid correspond to what we expect in a simple case
# AbstractMarginFunction.VISUALIZATION_RESOLUTION = 2
- # grid = self.margin_model.margin_function_sample.grid_2D['loc']
+ # grid = self.margin_model.margin_function.grid_2D['loc']
# true_grid = np.array([[0.98, 1.0], [1.0, 1.02]])
# self.assertTrue((grid == true_grid).all(), msg="\nexpected:\n{}, \nfound:\n{}".format(true_grid, grid))
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 59b0ee359b25db16c86ba8f46888764ae7328db0..dc1f602b5287583546dd01ecb0de01c7d5ca480e 100644
--- a/test/test_extreme_fit/test_model/test_margin_temporal.py
+++ b/test/test_extreme_fit/test_model/test_margin_temporal.py
@@ -56,7 +56,7 @@ class TestMarginTemporal(unittest.TestCase):
# By default, estimator find the good margin
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,
+ self.smooth_margin_model.margin_function.mu1_temporal_trend,
places=3)
# Checks starting point parameter are well passed
self.assertEqual(2, estimator.function_from_fit.starting_point)
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 dc4d8934ee39581a76d82f5a73c3cd0cd86d5f17..38591b03839d97c3fc80dd2c176a2dbeb369772f 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
@@ -61,7 +61,7 @@ class TestMaxStableTemporal(unittest.TestCase):
# By default, estimator find the good margin
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,
+ self.smooth_margin_model.margin_function.mu1_temporal_trend,
places=2)
# Checks starting point parameter are well passed
self.assertEqual(2, estimator.function_from_fit.starting_point)
diff --git a/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py b/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py
index a484b7abf6b8c2ec3230da4218903455ac5a1546..5b3e6f28572b4988924da469e844cd2d1b10d47a 100644
--- a/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py
+++ b/test/test_spatio_temporal_dataset/test_spatio_temporal_observations.py
@@ -63,7 +63,7 @@ class TestDailyObservations(unittest.TestCase):
def test_instance_exp_params(self):
last_coordinate = self.coordinates.coordinates_values()[-1]
- params = self.margin_model.margin_function_sample.get_params(last_coordinate)
+ params = self.margin_model.margin_function.get_params(last_coordinate)
self.assertIsInstance(params, ExpParams)
def test_exponential_observations(self):