diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
index 026f170809941466b5380ef5df40810f9ad8d96c..34bba367fef55a9f000a929bb6de6c2bb21dbf3d 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
@@ -77,7 +77,7 @@ def normal_visualization(temporal_non_stationarity=False):
study_visualizer = StudyVisualizer(study, save_to_file=save_to_file, temporal_non_stationarity=temporal_non_stationarity)
# study_visualizer.visualize_independent_margin_fits(threshold=[None, 20, 40, 60][0])
# study_visualizer.visualize_annual_mean_values()
- study_visualizer.visualize_linear_margin_fit(only_first_max_stable=None)
+ study_visualizer.visualize_linear_margin_fit(only_first_max_stable=True)
def complete_analysis(only_first_one=False):
diff --git a/extreme_estimator/estimator/full_estimator/abstract_full_estimator.py b/extreme_estimator/estimator/full_estimator/abstract_full_estimator.py
index 8ba3b479aeda79e59a24072a35c0874a194576fa..fa9c0e768dad34a20f8cbc5c43e3e4ec743505ec 100644
--- a/extreme_estimator/estimator/full_estimator/abstract_full_estimator.py
+++ b/extreme_estimator/estimator/full_estimator/abstract_full_estimator.py
@@ -56,8 +56,9 @@ class FullEstimatorInASingleStepWithSmoothMargin(AbstractFullEstimator):
def _fit(self):
# Estimate both the margin and the max-stable structure
self._result_from_fit = self.max_stable_model.fitmaxstab(
- maxima_gev=self.dataset.maxima_gev(split=self.train_split),
- df_coordinates=self.dataset.df_coordinates(split=self.train_split),
+ data_gev=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(self.train_split),
fit_marge=True,
fit_marge_form_dict=self.linear_margin_function_to_fit.form_dict,
margin_start_dict=self.linear_margin_function_to_fit.coef_dict
diff --git a/extreme_estimator/estimator/margin_estimator/abstract_margin_estimator.py b/extreme_estimator/estimator/margin_estimator/abstract_margin_estimator.py
index ab686e12a8b9fbff1a3c9c6fd2ea4386534abe0c..fc6025ec6bb66a9ed115f5a01d3b64a0cd8eaca6 100644
--- a/extreme_estimator/estimator/margin_estimator/abstract_margin_estimator.py
+++ b/extreme_estimator/estimator/margin_estimator/abstract_margin_estimator.py
@@ -38,10 +38,10 @@ class SmoothMarginEstimator(AbstractMarginEstimator):
self.margin_model = margin_model
def _fit(self):
- maxima_gev = self.dataset.maxima_gev(self.train_split)
+ 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(self.train_split)
- self._result_from_fit = self.margin_model.fitmargin_from_maxima_gev(maxima_gev=maxima_gev,
+ self._result_from_fit = self.margin_model.fitmargin_from_maxima_gev(data=maxima_gev_specialized,
df_coordinates_spat=df_coordinates_spat,
df_coordinates_temp=df_coordinates_temp)
self.extract_fitted_models_from_fitted_params(self.margin_model.margin_function_start_fit, self.fitted_values)
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 b749d3f746e727a1141da555653eac31deaf7fba..3468d859381123f6de079e6fc42620da286eb8a5 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
@@ -19,8 +19,8 @@ class MaxStableEstimator(AbstractMaxStableEstimator):
def _fit(self):
assert self.dataset.maxima_frech(split=self.train_split) is not None
self._result_from_fit = self.max_stable_model.fitmaxstab(
- maxima_frech=self.dataset.maxima_frech(split=self.train_split),
- df_coordinates=self.dataset.df_coordinates(split=self.train_split))
+ 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.fitted_values
def scalars(self, true_max_stable_params: dict):
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 79b203a11f6c89b1c55c6ca3f5ea42e7728f9e22..c9946d727a9b97585227afd6001bd1e1ebd9ae3b 100644
--- a/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
+++ b/extreme_estimator/extreme_models/margin_model/parametric_margin_model.py
@@ -20,17 +20,12 @@ class ParametricMarginModel(AbstractMarginModel, ABC):
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,
+ def fitmargin_from_maxima_gev(self, data: 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_spat), len(df_coordinates_temp)])
- data = np.transpose(maxima_gev)
assert data.shape[1] == len(df_coordinates_spat)
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)
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 231a46a85ed11f0fef1c8cf0d0bf86b0f2d35ed4..a3b136ee8dd478464f872d78f5e1580db88bb816 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
@@ -20,32 +20,30 @@ class AbstractMaxStableModel(AbstractModel):
def cov_mod_param(self):
return {'cov.mod': self.cov_mod}
- def fitmaxstab(self, df_coordinates: pd.DataFrame, maxima_frech: np.ndarray = None, maxima_gev: np.ndarray = None,
+ 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:
- assert isinstance(df_coordinates, pd.DataFrame)
+ assert isinstance(df_coordinates_spat, pd.DataFrame)
if fit_marge:
assert fit_marge_form_dict is not None
assert margin_start_dict is not None
# Prepare the data
- maxima = maxima_gev if fit_marge else maxima_frech
- assert isinstance(maxima, np.ndarray)
- assert len(df_coordinates) == len(maxima), 'Coordinates and observations sizes should match,' \
- 'check that the same split was used for both objects, \n' \
- 'df_coordinates size: {}, data size {}'.format(len(df_coordinates),
- len(maxima))
- data = np.transpose(maxima)
+ data = data_gev if fit_marge else data_frech
+ assert isinstance(data, np.ndarray)
+ assert len(df_coordinates_spat) == data.shape[1]
# Prepare the coord
- df_coordinates = df_coordinates.copy()
+ df_coordinates_spat = df_coordinates_spat.copy()
# In the one dimensional case, fitmaxstab isn't working
# therefore, we treat our 1D coordinate as 2D coordinate on the line y=x, and enforce iso=TRUE
- fitmaxstab_with_one_dimensional_data = len(df_coordinates.columns) == 1
+ fitmaxstab_with_one_dimensional_data = len(df_coordinates_spat.columns) == 1
if fitmaxstab_with_one_dimensional_data:
- assert AbstractCoordinates.COORDINATE_X in df_coordinates.columns
- df_coordinates[AbstractCoordinates.COORDINATE_Y] = df_coordinates[AbstractCoordinates.COORDINATE_X]
+ assert AbstractCoordinates.COORDINATE_X in df_coordinates_spat.columns
+ df_coordinates_spat[AbstractCoordinates.COORDINATE_Y] = df_coordinates_spat[
+ AbstractCoordinates.COORDINATE_X]
# Give names to columns to enable a specification of the shape of each marginal parameter
- coord = get_coord(df_coordinates)
+ coord = get_coord(df_coordinates_spat)
# Prepare the fit_params (a dictionary containing all additional parameters)
fit_params = self.cov_mod_param.copy()
@@ -61,8 +59,16 @@ class AbstractMaxStableModel(AbstractModel):
fit_params['start'] = r.list(**start_dict)
fit_params['fit.marge'] = fit_marge
+ # Add some temporal covariates
+ # Check the shape of the data
+ has_temp_cov = df_coordinates_temp is not None and len(df_coordinates_temp) > 0
+ if has_temp_cov:
+ assert len(df_coordinates_temp) == len(data)
+ fit_params['temp.cov'] = get_coord(df_coordinates_temp)
+
# Run the fitmaxstab in R
- return safe_run_r_estimator(function=r.fitmaxstab, use_start=self.use_start_value, data=data, coord=coord, **fit_params)
+ return safe_run_r_estimator(function=r.fitmaxstab, use_start=self.use_start_value, data=data, coord=coord,
+ **fit_params)
def rmaxstab(self, nb_obs: int, coordinates_values: np.ndarray) -> np.ndarray:
"""
@@ -86,7 +92,8 @@ class CovarianceFunction(Enum):
class AbstractMaxStableModelWithCovarianceFunction(AbstractMaxStableModel):
- def __init__(self, use_start_value=False, params_start_fit=None, params_sample=None, covariance_function: CovarianceFunction = None):
+ def __init__(self, use_start_value=False, params_start_fit=None, params_sample=None,
+ covariance_function: CovarianceFunction = None):
super().__init__(use_start_value, params_start_fit, params_sample)
assert covariance_function is not None
self.covariance_function = covariance_function
diff --git a/spatio_temporal_dataset/coordinates/abstract_coordinates.py b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
index 5b1a0ea14ddc9cadbbdf1cc0c0eb02f9b18e9dfd..f4653bd4b037959706f107437469bca34619493f 100644
--- a/spatio_temporal_dataset/coordinates/abstract_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
@@ -166,7 +166,7 @@ class AbstractCoordinates(object):
return len(self.coordinates_spatial_names)
@property
- def has_spatial_coordinates(self):
+ def has_spatial_coordinates(self) -> bool:
return self.nb_coordinates_spatial > 0
def df_spatial_coordinates(self, split: Split = Split.all) -> pd.DataFrame:
@@ -189,13 +189,9 @@ class AbstractCoordinates(object):
return len(self.coordinates_temporal_names)
@property
- def has_temporal_coordinates(self):
+ def has_temporal_coordinates(self) -> bool:
return self.nb_coordinates_temporal > 0
- @property
- def has_spatio_temporal_coordinates(self):
- return self.has_spatial_coordinates and self.has_temporal_coordinates
-
def df_temporal_coordinates(self, split: Split = Split.all) -> pd.DataFrame:
if self.nb_coordinates_temporal == 0:
return pd.DataFrame()
@@ -206,6 +202,15 @@ class AbstractCoordinates(object):
df_temporal_coordinates = self.df_temporal_coordinates(split)
return int(df_temporal_coordinates.min()), int(df_temporal_coordinates.max()),
+ # Spatio temporal attributes
+
+ @property
+ def has_spatio_temporal_coordinates(self) -> bool:
+ return self.has_spatial_coordinates and self.has_temporal_coordinates
+
+ def spatio_temporal_shape(self, split: Split.all) -> Tuple[int, int]:
+ return len(self.df_spatial_coordinates(split)), len(self.df_temporal_coordinates(split))
+
# Visualization
@property
diff --git a/spatio_temporal_dataset/dataset/abstract_dataset.py b/spatio_temporal_dataset/dataset/abstract_dataset.py
index 1ea1ba2c68cebad80c5d29aa6ff5749601483ef7..d18ed288969e58b53351ea5445e6d8906332e1c7 100644
--- a/spatio_temporal_dataset/dataset/abstract_dataset.py
+++ b/spatio_temporal_dataset/dataset/abstract_dataset.py
@@ -51,6 +51,20 @@ class AbstractDataset(object):
def set_maxima_frech(self, maxima_frech_values: np.ndarray, split: Split = Split.all):
self.observations.set_maxima_frech(maxima_frech_values, split, self.slicer)
+ # Observation wrapper for fit function
+
+ def transform_maxima_for_spatial_extreme_package(self, maxima_function, split) -> np.ndarray:
+ array = maxima_function(split)
+ if self.coordinates.has_spatio_temporal_coordinates:
+ array = array.reshape(self.coordinates.spatio_temporal_shape(split))
+ return np.transpose(array)
+
+ def maxima_gev_for_spatial_extremes_package(self, split: Split = Split.all) -> np.ndarray:
+ return self.transform_maxima_for_spatial_extreme_package(self.maxima_gev, split)
+
+ def maxima_frech_for_spatial_extremes_package(self, split: Split = Split.all) -> np.ndarray:
+ return self.transform_maxima_for_spatial_extreme_package(self.maxima_frech, split)
+
# Coordinates wrapper
def df_coordinates(self, split: Split = Split.all) -> pd.DataFrame: