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 d4ca048ff05cee419c60c8397101cac85855465e..c052dc184085846fef40f06d503bdeb5ff6e4e0d 100644
--- a/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/abstract_margin_model.py
@@ -1,17 +1,21 @@
+from abc import ABC
+
import numpy as np
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.utils import r
from extreme_estimator.margin_fits.gev.gev_params import GevParams
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
-class AbstractMarginModel(AbstractModel):
+class AbstractMarginModel(AbstractModel, ABC):
- def __init__(self, coordinates: AbstractCoordinates, use_start_value=False, params_start_fit=None, params_sample=None):
+ def __init__(self, coordinates: AbstractCoordinates, use_start_value=False,
+ params_start_fit=None, params_sample=None):
super().__init__(use_start_value, params_start_fit, params_sample)
assert isinstance(coordinates, AbstractCoordinates), type(coordinates)
self.coordinates = coordinates
@@ -20,7 +24,7 @@ class AbstractMarginModel(AbstractModel):
self.load_margin_functions()
def load_margin_functions(self):
- pass
+ raise NotImplementedError
def default_load_margin_functions(self, margin_function_class):
self.margin_function_sample = margin_function_class(coordinates=self.coordinates,
@@ -70,7 +74,7 @@ class AbstractMarginModel(AbstractModel):
# 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) -> AbstractMarginFunction:
+ df_coordinates_temporal: pd.DataFrame) -> ResultFromFit:
raise NotImplementedError
diff --git a/extreme_estimator/extreme_models/margin_model/linear_margin_model.py b/extreme_estimator/extreme_models/margin_model/linear_margin_model.py
index 1e77a4ea42be0516f77e79603284081dadf3f30c..25eeba991a0211c7ff43640ea9fe31d1b63d2ec9 100644
--- a/extreme_estimator/extreme_models/margin_model/linear_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/linear_margin_model.py
@@ -1,16 +1,11 @@
-import numpy as np
-import pandas as pd
-
-from extreme_estimator.extreme_models.result_from_fit import ResultFromFit
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.param_function.linear_coef import LinearCoef
-from extreme_estimator.extreme_models.utils import safe_run_r_estimator, r, get_coord, \
- get_margin_formula
+from extreme_estimator.extreme_models.margin_model.parametric_margin_model import ParametricMarginModel
from extreme_estimator.margin_fits.gev.gev_params import GevParams
-class LinearMarginModel(AbstractMarginModel):
+class LinearMarginModel(ParametricMarginModel):
def load_margin_functions(self, gev_param_name_to_linear_dims=None):
assert gev_param_name_to_linear_dims is not None, 'LinearMarginModel cannot be used for sampling/fitting \n' \
@@ -57,26 +52,6 @@ class LinearMarginModel(AbstractMarginModel):
params[(gev_param_name, dim)] = coef
return cls(coordinates, params_sample=params, params_start_fit=params)
- def fitmargin_from_maxima_gev(self, maxima_gev: np.ndarray, df_coordinates_spat: pd.DataFrame,
- df_coordinates_temp: pd.DataFrame) -> ResultFromFit:
- # The reshaping on the line below is only valid if we have a single observation per spatio-temporal point
- if maxima_gev.shape[1] == 1:
- maxima_gev = maxima_gev.reshape([len(df_coordinates_temp), len(df_coordinates_spat)])
- data = np.transpose(maxima_gev)
-
- fit_params = get_margin_formula(self.margin_function_start_fit.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
- fit_params['start'] = r.list(**coef_dict)
-
- return safe_run_r_estimator(function=r.fitspatgev, use_start=self.use_start_value, data=data,
- covariables=covariables, **fit_params)
-
class ConstantMarginModel(LinearMarginModel):
diff --git a/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py b/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py
index 6ae93485d6c9fc24d03f40fc4101b61c34c60eeb..44f704459e1b13374a0b144b6ea3b95531b89c05 100644
--- a/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py
+++ b/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py
@@ -46,7 +46,7 @@ class AbstractMarginFunction(object):
def get_gev_params(self, coordinate: np.ndarray) -> GevParams:
"""Main method that maps each coordinate to its GEV parameters"""
- pass
+ raise NotImplementedError
@property
def gev_value_name_to_serie(self) -> Dict[str, pd.Series]:
diff --git a/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py b/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
index 4196d16e51725d6a2c29e74628607772d07c69e4..34134e31cedbf5fe1d22fbb3f969276334051733 100644
--- a/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
+++ b/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
@@ -1,7 +1,7 @@
from typing import Dict, List
-from extreme_estimator.extreme_models.margin_model.margin_function.independent_margin_function import \
- IndependentMarginFunction
+from extreme_estimator.extreme_models.margin_model.margin_function.parametric_margin_function import \
+ ParametricMarginFunction
from extreme_estimator.extreme_models.margin_model.param_function.linear_coef import LinearCoef
from extreme_estimator.extreme_models.margin_model.param_function.param_function import ConstantParamFunction, \
ParamFunction, LinearParamFunction
@@ -9,7 +9,7 @@ from extreme_estimator.margin_fits.gev.gev_params import GevParams
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
-class LinearMarginFunction(IndependentMarginFunction):
+class LinearMarginFunction(ParametricMarginFunction):
""" Margin Function, where each parameter can augment linearly along any dimension.
dim = 0 correspond to the intercept
diff --git a/extreme_estimator/extreme_models/margin_model/margin_function/parametric_margin_function.py b/extreme_estimator/extreme_models/margin_model/margin_function/parametric_margin_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..0305bcfbfad0f7e69ab458fa508ebe830c59d654
--- /dev/null
+++ b/extreme_estimator/extreme_models/margin_model/margin_function/parametric_margin_function.py
@@ -0,0 +1,15 @@
+from typing import Dict
+
+from extreme_estimator.extreme_models.margin_model.margin_function.independent_margin_function import \
+ IndependentMarginFunction
+
+
+class ParametricMarginFunction(IndependentMarginFunction):
+
+ @property
+ def form_dict(self) -> Dict[str, str]:
+ raise NotImplementedError
+
+ @property
+ def coef_dict(self) -> Dict[str, float]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py b/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b26d7efb60d5564d2e5787a9a43fa8925639b659
--- /dev/null
+++ b/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
@@ -0,0 +1,41 @@
+from abc import ABC
+
+import numpy as np
+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
+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
+
+
+class ParametricMarginModel(AbstractMarginModel, ABC):
+
+ def __init__(self, coordinates: AbstractCoordinates, use_start_value=False, params_start_fit=None,
+ params_sample=None):
+ self.margin_function_sample = None # type: ParametricMarginFunction
+ self.margin_function_start_fit = None # type: ParametricMarginFunction
+ super().__init__(coordinates, use_start_value, params_start_fit, params_sample)
+
+ def fitmargin_from_maxima_gev(self, maxima_gev: np.ndarray, df_coordinates_spat: pd.DataFrame,
+ df_coordinates_temp: pd.DataFrame) -> ResultFromFit:
+ # The reshaping on the line below is only valid if we have a single observation per spatio-temporal point
+ if maxima_gev.shape[1] == 1:
+ maxima_gev = maxima_gev.reshape([len(df_coordinates_temp), len(df_coordinates_spat)])
+ data = np.transpose(maxima_gev)
+
+ fit_params = get_margin_formula(self.margin_function_start_fit.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
+ fit_params['start'] = r.list(**coef_dict)
+
+ return safe_run_r_estimator(function=r.fitspatgev, use_start=self.use_start_value, data=data,
+ covariables=covariables, **fit_params)
diff --git a/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py b/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py
deleted file mode 100644
index 1e77a4ea42be0516f77e79603284081dadf3f30c..0000000000000000000000000000000000000000
--- a/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import numpy as np
-import pandas as pd
-
-from extreme_estimator.extreme_models.result_from_fit import ResultFromFit
-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.param_function.linear_coef import LinearCoef
-from extreme_estimator.extreme_models.utils import safe_run_r_estimator, r, get_coord, \
- get_margin_formula
-from extreme_estimator.margin_fits.gev.gev_params import GevParams
-
-
-class LinearMarginModel(AbstractMarginModel):
-
- def load_margin_functions(self, gev_param_name_to_linear_dims=None):
- assert gev_param_name_to_linear_dims is not None, 'LinearMarginModel cannot be used for sampling/fitting \n' \
- 'load_margin_functions needs to be implemented in child class'
- # Load sample coef
- self.default_params_sample = self.default_param_name_and_dim_to_coef()
- linear_coef_sample = self.gev_param_name_to_linear_coef(param_name_and_dim_to_coef=self.params_sample)
- self.margin_function_sample = LinearMarginFunction(coordinates=self.coordinates,
- gev_param_name_to_linear_coef=linear_coef_sample,
- gev_param_name_to_linear_dims=gev_param_name_to_linear_dims)
-
- # Load start fit coef
- self.default_params_start_fit = self.default_param_name_and_dim_to_coef()
- linear_coef_start_fit = self.gev_param_name_to_linear_coef(param_name_and_dim_to_coef=self.params_start_fit)
- self.margin_function_start_fit = LinearMarginFunction(coordinates=self.coordinates,
- gev_param_name_to_linear_coef=linear_coef_start_fit,
- gev_param_name_to_linear_dims=gev_param_name_to_linear_dims)
-
- @staticmethod
- def default_param_name_and_dim_to_coef() -> dict:
- default_intercept = 1
- default_slope = 0.01
- gev_param_name_and_dim_to_coef = {}
- for gev_param_name in GevParams.PARAM_NAMES:
- gev_param_name_and_dim_to_coef[(gev_param_name, 0)] = default_intercept
- for dim in [1, 2, 3]:
- gev_param_name_and_dim_to_coef[(gev_param_name, dim)] = default_slope
- return gev_param_name_and_dim_to_coef
-
- @staticmethod
- def gev_param_name_to_linear_coef(param_name_and_dim_to_coef):
- gev_param_name_to_linear_coef = {}
- for gev_param_name in GevParams.PARAM_NAMES:
- dim_to_coef = {dim: param_name_and_dim_to_coef[(gev_param_name, dim)] for dim in [0, 1, 2, 3]}
- linear_coef = LinearCoef(gev_param_name=gev_param_name, dim_to_coef=dim_to_coef)
- gev_param_name_to_linear_coef[gev_param_name] = linear_coef
- return gev_param_name_to_linear_coef
-
- @classmethod
- def from_coef_list(cls, coordinates, gev_param_name_to_coef_list):
- params = {}
- for gev_param_name in GevParams.PARAM_NAMES:
- for dim, coef in enumerate(gev_param_name_to_coef_list[gev_param_name]):
- params[(gev_param_name, dim)] = coef
- return cls(coordinates, params_sample=params, params_start_fit=params)
-
- def fitmargin_from_maxima_gev(self, maxima_gev: np.ndarray, df_coordinates_spat: pd.DataFrame,
- df_coordinates_temp: pd.DataFrame) -> ResultFromFit:
- # The reshaping on the line below is only valid if we have a single observation per spatio-temporal point
- if maxima_gev.shape[1] == 1:
- maxima_gev = maxima_gev.reshape([len(df_coordinates_temp), len(df_coordinates_spat)])
- data = np.transpose(maxima_gev)
-
- fit_params = get_margin_formula(self.margin_function_start_fit.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
- fit_params['start'] = r.list(**coef_dict)
-
- return safe_run_r_estimator(function=r.fitspatgev, use_start=self.use_start_value, data=data,
- covariables=covariables, **fit_params)
-
-
-class ConstantMarginModel(LinearMarginModel):
-
- def load_margin_functions(self, gev_param_name_to_linear_dims=None):
- super().load_margin_functions({})
-
-
-class LinearShapeDim1MarginModel(LinearMarginModel):
-
- def load_margin_functions(self, margin_function_class: type = None, gev_param_name_to_linear_dims=None):
- super().load_margin_functions({GevParams.SHAPE: [1]})
-
-
-class LinearScaleDim1MarginModel(LinearMarginModel):
-
- def load_margin_functions(self, margin_function_class: type = None, gev_param_name_to_linear_dims=None):
- super().load_margin_functions({GevParams.SCALE: [1]})
-
-
-class LinearShapeDim1and2MarginModel(LinearMarginModel):
-
- def load_margin_functions(self, margin_function_class: type = None, gev_param_name_to_linear_dims=None):
- super().load_margin_functions({GevParams.SHAPE: [1, 2]})
-
-
-class LinearAllParametersDim1MarginModel(LinearMarginModel):
-
- def load_margin_functions(self, margin_function_class: type = None, gev_param_name_to_linear_dims=None):
- super().load_margin_functions({GevParams.SHAPE: [1],
- GevParams.LOC: [1],
- GevParams.SCALE: [1]})
-
-
-class LinearMarginModelExample(LinearMarginModel):
-
- def load_margin_functions(self, margin_function_class: type = None, gev_param_name_to_linear_dims=None):
- super().load_margin_functions({GevParams.SHAPE: [1],
- GevParams.LOC: [2],
- GevParams.SCALE: [1]})
-
-
-class LinearAllParametersAllDimsMarginModel(LinearMarginModel):
-
- def load_margin_functions(self, margin_function_class: type = None, gev_param_name_to_linear_dims=None):
- all_dims = list(range(1, self.coordinates.nb_coordinates + 1))
- super().load_margin_functions({GevParams.SHAPE: all_dims.copy(),
- GevParams.LOC: all_dims.copy(),
- GevParams.SCALE: all_dims.copy()})
diff --git a/test/test_extreme_estimator/test_extreme_models/test_margin_model.py b/test/test_extreme_estimator/test_extreme_models/test_margin_model.py
index 552a4b2419dcfc3b44ef4940f6a946f27748893d..5980064c76c6fa338443e8aec27de97b8f006e5b 100644
--- a/test/test_extreme_estimator/test_extreme_models/test_margin_model.py
+++ b/test/test_extreme_estimator/test_extreme_models/test_margin_model.py
@@ -4,6 +4,7 @@ import unittest
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 extreme_estimator.extreme_models.margin_model.spline_margin_model import SplineMarginModel
from extreme_estimator.margin_fits.gev.gev_params import GevParams
from extreme_estimator.extreme_models.margin_model.linear_margin_model import LinearShapeDim1MarginModel, \
LinearAllParametersAllDimsMarginModel
@@ -14,15 +15,17 @@ from spatio_temporal_dataset.coordinates.spatial_coordinates.coordinates_1D impo
from test.test_utils import load_test_spatiotemporal_coordinates
-class TestVisualizationMarginModel(unittest.TestCase):
+class TestVisualizationLinearMarginModel(unittest.TestCase):
DISPLAY = False
nb_points = 2
- margin_model_class = [LinearShapeDim1MarginModel, LinearAllParametersAllDimsMarginModel][-1]
+ margin_model_class = LinearAllParametersAllDimsMarginModel
def tearDown(self) -> None:
self.margin_model.margin_function_sample.visualize_function(show=self.DISPLAY)
self.assertTrue(True)
+
+
def test_example_visualization_1D(self):
coordinates = LinSpaceSpatialCoordinates.from_nb_points(nb_points=self.nb_points)
self.margin_model = self.margin_model_class(coordinates=coordinates, params_sample={(GevParams.SHAPE, 1): 0.02})
@@ -56,6 +59,27 @@ class TestVisualizationMarginModel(unittest.TestCase):
# # Load
+# class TestVisualizationSplineMarginModel(TestVisualizationMarginModel):
+# margin_model_class = SplineMarginModel
+#
+# def tearDown(self) -> None:
+# self.margin_model.margin_function_sample.visualize_function(show=self.DISPLAY)
+# self.assertTrue(True)
+
+ # def test_example_visualization_1D_spline(self):
+ # coordinates = LinSpaceSpatialCoordinates.from_nb_points(nb_points=self.nb_points)
+ # self.margin_model = self.margin_model_class(coordinates=coordinates, params_sample={(GevParams.SHAPE, 1): 0.02})
+
+ # def test_example_visualization_2D_spatial_spline(self):
+ # spatial_coordinates = LinSpaceSpatial2DCoordinates.from_nb_points(nb_points=self.nb_points)
+ # self.margin_model = self.margin_model_class(coordinates=spatial_coordinates)
+ # # 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']
+ # 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))
+
+
if __name__ == '__main__':
unittest.main()
# v = TestVisualizationMarginModel()