diff --git a/experiment/regression_margin/regression_margin.py b/experiment/regression_margin/regression_margin.py
index bb96a5dfbae56a181a5409a10092ef6bedd1233e..a46fd2022bbbe8687164a15e7f2e19d406f98314 100644
--- a/experiment/regression_margin/regression_margin.py
+++ b/experiment/regression_margin/regression_margin.py
@@ -1,8 +1,11 @@
+import random
+
import numpy as np
from extreme_estimator.estimator.full_estimator import FullEstimatorInASingleStepWithSmoothMargin
-from extreme_estimator.extreme_models.margin_model.smooth_margin_model import LinearShapeAxis0MarginModel, \
- LinearAllParametersAllAxisMarginModel
+from extreme_estimator.extreme_models.margin_model.margin_function.linear_margin_function import LinearMarginFunction
+from extreme_estimator.extreme_models.margin_model.smooth_margin_model import LinearShapeDim1MarginModel, \
+ LinearAllParametersAllDimsMarginModel
from extreme_estimator.extreme_models.max_stable_model.max_stable_models import Smith
from extreme_estimator.gev_params import GevParams
from spatio_temporal_dataset.coordinates.unidimensional_coordinates.coordinates_1D import LinSpaceCoordinates
@@ -12,6 +15,7 @@ from spatio_temporal_dataset.dataset.simulation_dataset import FullSimulatedData
nb_points = 5
nb_obs = 10
+nb_estimator = 2
show = False
coordinates = LinSpaceCoordinates.from_nb_points(nb_points=nb_points)
@@ -19,25 +23,40 @@ coordinates = LinSpaceCoordinates.from_nb_points(nb_points=nb_points)
########## GENERATING THE DATA #####################
# MarginModel Linear with respect to the shape (from 0.01 to 0.02)
-margin_model = LinearShapeAxis0MarginModel(coordinates=coordinates, params_sample={GevParams.GEV_SHAPE: 0.02})
+params_sample = {
+ (GevParams.GEV_SHAPE, 0): 0.2,
+ (GevParams.GEV_SHAPE, 1): 0.05,
+}
+margin_model = LinearShapeDim1MarginModel(coordinates=coordinates, params_sample=params_sample)
max_stable_model = Smith()
-dataset = FullSimulatedDataset.from_double_sampling(nb_obs=nb_obs, margin_model=margin_model,
- coordinates=coordinates,
- max_stable_model=max_stable_model)
-if show:
- # Visualize the sampling margin
- dataset.margin_model.margin_function_sample.visualize_all()
- # Plot a realization from the maxima gev (i.e the maxima obtained just by simulating the marginal law)
- for maxima_gev in np.transpose(dataset.maxima_gev):
- plt.plot(coordinates.coordinates_values, maxima_gev)
- plt.show()
+
+# if show:
+# # Plot a realization from the maxima gev (i.e the maxima obtained just by simulating the marginal law)
+# for maxima_gev in np.transpose(dataset.maxima_gev):
+# plt.plot(coordinates.coordinates_values, maxima_gev)
+# plt.show()
######### FITTING A MODEL #################
-margin_model = LinearAllParametersAllAxisMarginModel(coordinates)
-full_estimator = FullEstimatorInASingleStepWithSmoothMargin(dataset, margin_model, max_stable_model)
-full_estimator.fit()
-full_estimator.margin_function_fitted.visualize_all()
+
+axes = None
+for _ in range(nb_estimator):
+ # Data part
+ dataset = FullSimulatedDataset.from_double_sampling(nb_obs=nb_obs, margin_model=margin_model,
+ coordinates=coordinates,
+ max_stable_model=max_stable_model)
+ margin_function_sample = dataset.margin_model.margin_function_sample # type: LinearMarginFunction
+ margin_function_sample.visualize(show=False, axes=axes)
+ axes = margin_function_sample.visualization_axes
+
+ # Estimation part
+ margin_model_for_estimator = LinearAllParametersAllDimsMarginModel(coordinates)
+ full_estimator = FullEstimatorInASingleStepWithSmoothMargin(dataset, margin_model_for_estimator, max_stable_model)
+ full_estimator.fit()
+ full_estimator.margin_function_fitted.visualize(axes=axes, show=False)
+plt.show()
+
+# Display all the margin on the same graph for comparison
# Plot the margin functions
# margin_model.margin_function_sample.visualize_2D()
diff --git a/experiment/regression_margin/visualization_margin_model.py b/experiment/regression_margin/visualization_margin_model.py
index 1f405c9d357c91940cb79f2f5a6c7ebe722425f6..b5f548b32dd9344d0b26cb0453de460a2752099d 100644
--- a/experiment/regression_margin/visualization_margin_model.py
+++ b/experiment/regression_margin/visualization_margin_model.py
@@ -1,8 +1,8 @@
import unittest
from extreme_estimator.gev_params import GevParams
-from extreme_estimator.extreme_models.margin_model.smooth_margin_model import LinearShapeAxis0MarginModel, \
- LinearAllParametersAllAxisMarginModel
+from extreme_estimator.extreme_models.margin_model.smooth_margin_model import LinearShapeDim1MarginModel, \
+ LinearAllParametersAllDimsMarginModel
from spatio_temporal_dataset.coordinates.spatial_coordinates.generated_spatial_coordinates import CircleCoordinates
from spatio_temporal_dataset.coordinates.unidimensional_coordinates.coordinates_1D import LinSpaceCoordinates
@@ -10,14 +10,14 @@ from spatio_temporal_dataset.coordinates.unidimensional_coordinates.coordinates_
class VisualizationMarginModel(unittest.TestCase):
DISPLAY = True
nb_points = 50
- margin_model = [LinearShapeAxis0MarginModel, LinearAllParametersAllAxisMarginModel][-1]
+ margin_model = [LinearShapeDim1MarginModel, LinearAllParametersAllDimsMarginModel][-1]
@classmethod
def example_visualization_2D(cls):
spatial_coordinates = CircleCoordinates.from_nb_points(nb_points=cls.nb_points)
margin_model = cls.margin_model(coordinates=spatial_coordinates)
if cls.DISPLAY:
- margin_model.margin_function_sample.visualize_all()
+ margin_model.margin_function_sample.visualize()
@classmethod
def example_visualization_1D(cls):
@@ -25,7 +25,7 @@ class VisualizationMarginModel(unittest.TestCase):
# MarginModel Linear with respect to the shape (from 0.01 to 0.02)
margin_model = cls.margin_model(coordinates=coordinates, params_sample={GevParams.GEV_SHAPE: 0.02})
if cls.DISPLAY:
- margin_model.margin_function_sample.visualize_all()
+ margin_model.margin_function_sample.visualize()
if __name__ == '__main__':
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 090f8f3385b8cf041029680d67e25fb5aa4ada63..8fcbc504491a971e0036fa7fff76c3db965ada5c 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
@@ -11,6 +11,7 @@ class AbstractMarginFunction(object):
def __init__(self, coordinates: AbstractCoordinates):
self.coordinates = coordinates
+ self.visualization_axes = None
def get_gev_params(self, coordinate: np.ndarray) -> GevParams:
"""Main method that maps each coordinate to its GEV parameters"""
@@ -18,17 +19,20 @@ class AbstractMarginFunction(object):
# Visualization function
- def visualize_all(self):
- fig, axes = plt.subplots(3, 1, sharex='col', sharey='row')
- fig.subplots_adjust(hspace=0.4, wspace=0.4, )
+ def visualize(self, axes=None, show=True):
+ if axes is None:
+ fig, axes = plt.subplots(3, 1, sharex='col', sharey='row')
+ fig.subplots_adjust(hspace=0.4, wspace=0.4, )
+ self.visualization_axes = axes
for i, gev_param_name in enumerate(GevParams.GEV_PARAM_NAMES):
ax = axes[i]
- self.visualize(gev_param_name, ax, show=False)
+ self.visualize_single_param(gev_param_name, ax, show=False)
title_str = gev_param_name
ax.set_title(title_str)
- plt.show()
+ if show:
+ plt.show()
- def visualize(self, gev_param_name=GevParams.GEV_LOC, ax=None, show=True):
+ def visualize_single_param(self, gev_param_name=GevParams.GEV_LOC, ax=None, show=True):
if self.coordinates.nb_columns == 1:
self.visualize_1D(gev_param_name, ax, show)
elif self.coordinates.nb_columns == 2:
diff --git a/extreme_estimator/extreme_models/margin_model/param_function/param_function.py b/extreme_estimator/extreme_models/margin_model/param_function/param_function.py
index df9f485a2c25c99e53fadb4e54f134407f9faeff..ad8555ef2d0bc5bc4e7bd33de8cf66eb49889ee4 100644
--- a/extreme_estimator/extreme_models/margin_model/param_function/param_function.py
+++ b/extreme_estimator/extreme_models/margin_model/param_function/param_function.py
@@ -26,11 +26,13 @@ class LinearOneAxisParamFunction(ParamFunction):
self.t_max = coordinates[:, linear_axis].max()
self.coef = coef
+ def get_gev_param_value_normalized(self, coordinate: np.ndarray) -> float:
+ return self.get_gev_param_value(coordinate) / (self.t_max - self.t_min)
+
def get_gev_param_value(self, coordinate: np.ndarray) -> float:
t = coordinate[self.linear_axis]
- t_between_zero_and_one = t / (self.t_max - self.t_min)
- assert -1 <= t_between_zero_and_one <= 1, 'Out of bounds'
- return t_between_zero_and_one * self.coef
+ assert self.t_min <= t <= self.t_max, 'Out of bounds'
+ return t * self.coef
class LinearParamFunction(ParamFunction):
@@ -40,7 +42,7 @@ class LinearParamFunction(ParamFunction):
# Load each one axis linear function
self.linear_one_axis_param_functions = [] # type: List[LinearOneAxisParamFunction]
for linear_dim in linear_dims:
- param_function = LinearOneAxisParamFunction(linear_axis=linear_dim-1, coordinates=coordinates,
+ param_function = LinearOneAxisParamFunction(linear_axis=linear_dim - 1, coordinates=coordinates,
coef=self.linear_coef.get_coef(dim=linear_dim))
self.linear_one_axis_param_functions.append(param_function)
diff --git a/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py b/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py
index 83e5f6d0ebd99f6be2833fc20d9039a8f0b606dd..b1115f6ca1d46b4e8f7d28f55bad4861f603a4f1 100644
--- a/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/smooth_margin_model.py
@@ -12,31 +12,41 @@ class LinearMarginModel(AbstractMarginModel):
def load_margin_functions(self, gev_param_name_to_linear_dims=None):
# Load sample coef
- self.default_params_sample = GevParams(1.0, 1.0, 1.0).to_dict()
- linear_coef_sample = self.get_standard_linear_coef(gev_param_name_to_intercept=self.params_sample)
+ 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 = GevParams(1.0, 1.0, 1.0).to_dict()
- linear_coef_start_fit = self.get_standard_linear_coef(gev_param_name_to_intercept=self.params_start_fit)
+ 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 get_standard_linear_coef(gev_param_name_to_intercept, slope=0.1):
+ 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.GEV_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.GEV_PARAM_NAMES:
- dim_to_coef = {dim: slope for dim in range(1, 4)}
- dim_to_coef[0] = gev_param_name_to_intercept[gev_param_name]
+ 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
-
- def fitmargin_from_maxima_gev(self, maxima_gev: np.ndarray, coordinates_values: np.ndarray) -> AbstractMarginFunction:
+ def fitmargin_from_maxima_gev(self, maxima_gev: np.ndarray,
+ coordinates_values: np.ndarray) -> AbstractMarginFunction:
return self.margin_function_start_fit
@@ -46,19 +56,19 @@ class ConstantMarginModel(LinearMarginModel):
super().load_margin_functions({})
-class LinearShapeAxis0MarginModel(LinearMarginModel):
+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.GEV_SHAPE: [1]})
-class LinearShapeAxis0and1MarginModel(LinearMarginModel):
+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.GEV_SHAPE: [1, 2]})
-class LinearAllParametersAxis0MarginModel(LinearMarginModel):
+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.GEV_SHAPE: [1],
@@ -66,7 +76,7 @@ class LinearAllParametersAxis0MarginModel(LinearMarginModel):
GevParams.GEV_SCALE: [1]})
-class LinearAllParametersAllAxisMarginModel(LinearMarginModel):
+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_columns + 1))
diff --git a/extreme_estimator/extreme_models/utils.py b/extreme_estimator/extreme_models/utils.py
index 513d50c7506a84f768a2a33082d9065a9b66f26f..9b1cb54862d9e080822ab126a93f93199688d3e9 100644
--- a/extreme_estimator/extreme_models/utils.py
+++ b/extreme_estimator/extreme_models/utils.py
@@ -1,4 +1,6 @@
import os.path as op
+import random
+import sys
import rpy2.robjects as ro
from rpy2.robjects import numpy2ri
@@ -10,6 +12,9 @@ def get_loaded_r() -> ro.R:
numpy2ri.activate()
pandas2ri.activate()
r.library('SpatialExtremes')
+ # max_int = r('.Machine$integer.max')
+ # seed = random.randrange(max_int)
+ # r("set.seed({})".format(seed))
return r
diff --git a/test/test_utils.py b/test/test_utils.py
index 84ec34eee67f588876f86c4e507311fe744b7f44..5abfc72ae43bd69bd8c3bd5945081f7d5c8f2fb2 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -1,7 +1,7 @@
from extreme_estimator.estimator.full_estimator import SmoothMarginalsThenUnitaryMsp, \
FullEstimatorInASingleStepWithSmoothMargin
from extreme_estimator.estimator.max_stable_estimator import MaxStableEstimator
-from extreme_estimator.extreme_models.margin_model.smooth_margin_model import LinearAllParametersAllAxisMarginModel, \
+from extreme_estimator.extreme_models.margin_model.smooth_margin_model import LinearAllParametersAllDimsMarginModel, \
ConstantMarginModel
from extreme_estimator.extreme_models.max_stable_model.abstract_max_stable_model import \
AbstractMaxStableModelWithCovarianceFunction, CovarianceFunction
@@ -21,7 +21,7 @@ In this case, unit test (at least on the constructor) must be ensured in the tes
TEST_MAX_STABLE_MODEL = [Smith, BrownResnick, Schlather, Geometric, ExtremalT, ISchlather]
TEST_1D_AND_2D_COORDINATES = [UniformCoordinates, CircleCoordinates]
TEST_3D_COORDINATES = [AlpsStation3DCoordinatesWithAnisotropy]
-TEST_MARGIN_TYPES = [ConstantMarginModel, LinearAllParametersAllAxisMarginModel][:]
+TEST_MARGIN_TYPES = [ConstantMarginModel, LinearAllParametersAllDimsMarginModel][:]
TEST_MAX_STABLE_ESTIMATOR = [MaxStableEstimator]
TEST_FULL_ESTIMATORS = [SmoothMarginalsThenUnitaryMsp, FullEstimatorInASingleStepWithSmoothMargin][:]