From 3914f93e8608c603e1857320a48e3cab2d51f5a9 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Sun, 19 Apr 2020 19:13:27 +0200
Subject: [PATCH] [refactor] modify margin function
---
.../parametric_margin_function.py | 4 +
extreme_fit/model/abstract_model.py | 14 ++--
.../margin_model/abstract_margin_model.py | 20 +++--
.../abstract_temporal_linear_margin_model.py | 3 +-
.../linear_margin_model.py | 78 +++++++++----------
.../temporal_linear_margin_exp_models.py | 4 +-
.../temporal_linear_margin_models.py | 26 +++----
.../margin_model/parametric_margin_model.py | 11 ++-
.../model/margin_model/spline_margin_model.py | 37 ++++-----
9 files changed, 101 insertions(+), 96 deletions(-)
diff --git a/extreme_fit/function/margin_function/parametric_margin_function.py b/extreme_fit/function/margin_function/parametric_margin_function.py
index e47e4054..ccf2a83d 100644
--- a/extreme_fit/function/margin_function/parametric_margin_function.py
+++ b/extreme_fit/function/margin_function/parametric_margin_function.py
@@ -91,3 +91,7 @@ class ParametricMarginFunction(IndependentMarginFunction):
@property
def form_dict(self) -> Dict[str, str]:
raise NotImplementedError
+
+ @property
+ def coef_dict(self) -> Dict[str, str]:
+ raise NotImplementedError
diff --git a/extreme_fit/model/abstract_model.py b/extreme_fit/model/abstract_model.py
index cd6e7355..640fe738 100644
--- a/extreme_fit/model/abstract_model.py
+++ b/extreme_fit/model/abstract_model.py
@@ -1,7 +1,7 @@
class AbstractModel(object):
def __init__(self, params_user=None):
- self.user_params_sample = params_user
+ self.params_user = params_user
@property
def default_params(self):
@@ -9,14 +9,14 @@ class AbstractModel(object):
@property
def params_sample(self) -> dict:
- return self.merge_params(default_params=self.default_params, input_params=self.user_params_sample)
+ return self.merge_params(default_params=self.default_params, params_user=self.params_user)
@staticmethod
- def merge_params(default_params, input_params):
+ def merge_params(default_params, params_user):
assert default_params is not None, 'some default_params need to be specified'
merged_params = default_params.copy()
- if input_params is not None:
- assert isinstance(default_params, dict) and isinstance(input_params, dict)
- assert set(input_params.keys()).issubset(set(default_params.keys()))
- merged_params.update(input_params)
+ if params_user is not None:
+ assert isinstance(default_params, dict) and isinstance(params_user, dict)
+ assert set(params_user.keys()).issubset(set(default_params.keys()))
+ merged_params.update(params_user)
return merged_params
diff --git a/extreme_fit/model/margin_model/abstract_margin_model.py b/extreme_fit/model/margin_model/abstract_margin_model.py
index d87146e6..33b22ddc 100644
--- a/extreme_fit/model/margin_model/abstract_margin_model.py
+++ b/extreme_fit/model/margin_model/abstract_margin_model.py
@@ -2,6 +2,7 @@ from abc import ABC
import numpy as np
import pandas as pd
+from cached_property import cached_property
from extreme_fit.model.abstract_model import AbstractModel
from extreme_fit.function.margin_function.abstract_margin_function \
@@ -18,23 +19,20 @@ class AbstractMarginModel(AbstractModel, ABC):
-margin_function
"""
- def __init__(self, coordinates: AbstractCoordinates, params_user=None,
- params_class=GevParams):
+ def __init__(self, coordinates: AbstractCoordinates, params_user=None, params_class=GevParams):
super().__init__(params_user)
assert isinstance(coordinates, AbstractCoordinates), type(coordinates)
self.coordinates = coordinates
- self.margin_function = None # type: AbstractMarginFunction
- self.margin_function = None # type: AbstractMarginFunction
self.params_class = params_class
- self.load_margin_functions()
- def load_margin_functions(self):
- raise NotImplementedError
+ @cached_property
+ def margin_function(self) -> AbstractMarginFunction:
+ margin_function = self.load_margin_function()
+ assert margin_function is not None
+ return margin_function
- def default_load_margin_functions(self, margin_function_class):
- # todo: check it i could remove these attributes
- self.margin_function = margin_function_class(coordinates=self.coordinates,
- default_params=self.params_class.from_dict(self.params_sample))
+ def load_margin_function(self):
+ raise NotImplementedError
# Conversion class methods
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 18c2afd5..c0901173 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
@@ -26,8 +26,7 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
params_initial_fit_bayesian=None,
type_for_MLE="GEV",
params_class=GevParams):
- super().__init__(coordinates, params_user, starting_point,
- params_class)
+ super().__init__(coordinates, params_user, starting_point, params_class)
self.type_for_mle = type_for_MLE
self.params_initial_fit_bayesian = params_initial_fit_bayesian
self.nb_iterations_for_bayesian_fit = nb_iterations_for_bayesian_fit
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 e3c86ebf..07d6e8cf 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
@@ -14,16 +14,16 @@ class LinearMarginModel(ParametricMarginModel):
params[(param_name, idx)] = coef
return cls(coordinates, params_user=params, params_class=params_class, **kwargs)
- def load_margin_functions(self, param_name_to_dims=None):
+ def load_margin_function(self, param_name_to_dims=None):
assert param_name_to_dims is not None, 'LinearMarginModel cannot be used for sampling/fitting \n' \
- 'load_margin_functions needs to be implemented in child class'
+ 'load_margin_functions needs to be implemented in child class'
# Load sample coef
coef_sample = self.param_name_to_linear_coef(param_name_and_dim_to_coef=self.params_sample)
- 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)
+ return 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_params(self) -> dict:
@@ -49,75 +49,75 @@ class LinearMarginModel(ParametricMarginModel):
class ConstantMarginModel(LinearMarginModel):
- def load_margin_functions(self, param_name_to_dims=None):
- super().load_margin_functions({})
+ def load_margin_function(self, param_name_to_dims=None):
+ return super().load_margin_function({})
class LinearShapeDim0MarginModel(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SHAPE: [0]})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SHAPE: [0]})
class LinearScaleDim0MarginModel(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SCALE: [0]})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SCALE: [0]})
class LinearShapeDim0and1MarginModel(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SHAPE: [0, 1]})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SHAPE: [0, 1]})
class LinearAllParametersDim0MarginModel(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SHAPE: [0],
- GevParams.LOC: [0],
- GevParams.SCALE: [0]})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SHAPE: [0],
+ GevParams.LOC: [0],
+ GevParams.SCALE: [0]})
class LinearMarginModelExample(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SHAPE: [0],
- GevParams.LOC: [1],
- GevParams.SCALE: [0]})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SHAPE: [0],
+ GevParams.LOC: [1],
+ GevParams.SCALE: [0]})
class LinearLocationAllDimsMarginModel(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.LOC: self.coordinates.coordinates_dims})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.LOC: self.coordinates.coordinates_dims})
class LinearShapeAllDimsMarginModel(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SHAPE: self.coordinates.coordinates_dims})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SHAPE: self.coordinates.coordinates_dims})
class LinearAllParametersAllDimsMarginModel(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SHAPE: self.coordinates.coordinates_dims,
- GevParams.LOC: self.coordinates.coordinates_dims,
- GevParams.SCALE: self.coordinates.coordinates_dims})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SHAPE: self.coordinates.coordinates_dims,
+ GevParams.LOC: self.coordinates.coordinates_dims,
+ GevParams.SCALE: self.coordinates.coordinates_dims})
class LinearStationaryMarginModel(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SHAPE: self.coordinates.spatial_coordinates_dims,
- GevParams.LOC: self.coordinates.spatial_coordinates_dims,
- GevParams.SCALE: self.coordinates.spatial_coordinates_dims})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SHAPE: self.coordinates.spatial_coordinates_dims,
+ GevParams.LOC: self.coordinates.spatial_coordinates_dims,
+ GevParams.SCALE: self.coordinates.spatial_coordinates_dims})
class LinearNonStationaryLocationMarginModel(LinearMarginModel):
- def load_margin_functions(self, margin_function_class: type = None, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SHAPE: self.coordinates.spatial_coordinates_dims,
- GevParams.LOC: self.coordinates.coordinates_dims,
- GevParams.SCALE: self.coordinates.spatial_coordinates_dims})
+ def load_margin_function(self, margin_function_class: type = None, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SHAPE: self.coordinates.spatial_coordinates_dims,
+ GevParams.LOC: self.coordinates.coordinates_dims,
+ GevParams.SCALE: self.coordinates.spatial_coordinates_dims})
diff --git a/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_exp_models.py b/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_exp_models.py
index 0204a860..2bf516b4 100644
--- a/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_exp_models.py
+++ b/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_exp_models.py
@@ -11,5 +11,5 @@ class NonStationaryRateTemporalModel(AbstractTemporalLinearMarginModel, Abstract
super().__init__(*arg, **kwargs)
self.drop_duplicates = False
- def load_margin_functions(self, param_name_to_dims=None):
- super().load_margin_functions({ExpParams.RATE: [self.coordinates.idx_temporal_coordinates]})
+ def load_margin_function(self, param_name_to_dims=None):
+ return super().load_margin_function({ExpParams.RATE: [self.coordinates.idx_temporal_coordinates]})
diff --git a/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_models.py b/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_models.py
index b84dcd7f..989db471 100644
--- a/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_models.py
+++ b/extreme_fit/model/margin_model/linear_margin_model/temporal_linear_margin_models.py
@@ -9,14 +9,14 @@ from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoo
class StationaryTemporalModel(AbstractTemporalLinearMarginModel):
- def load_margin_functions(self, param_name_to_dims=None):
- super().load_margin_functions({})
+ def load_margin_function(self, param_name_to_dims=None):
+ return super().load_margin_function({})
class NonStationaryLocationTemporalModel(AbstractTemporalLinearMarginModel):
- def load_margin_functions(self, param_name_to_dims=None):
- super().load_margin_functions({GevParams.LOC: [self.coordinates.idx_temporal_coordinates]})
+ def load_margin_function(self, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.LOC: [self.coordinates.idx_temporal_coordinates]})
@property
def mul(self):
@@ -25,8 +25,8 @@ class NonStationaryLocationTemporalModel(AbstractTemporalLinearMarginModel):
class NonStationaryScaleTemporalModel(AbstractTemporalLinearMarginModel):
- def load_margin_functions(self, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SCALE: [self.coordinates.idx_temporal_coordinates]})
+ def load_margin_function(self, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SCALE: [self.coordinates.idx_temporal_coordinates]})
@property
def sigl(self):
@@ -35,8 +35,8 @@ class NonStationaryScaleTemporalModel(AbstractTemporalLinearMarginModel):
class NonStationaryLogScaleTemporalModel(NonStationaryScaleTemporalModel):
- def load_margin_functions(self, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SCALE: [self.coordinates.idx_temporal_coordinates]})
+ def load_margin_function(self, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SCALE: [self.coordinates.idx_temporal_coordinates]})
@property
def siglink(self):
@@ -45,8 +45,8 @@ class NonStationaryLogScaleTemporalModel(NonStationaryScaleTemporalModel):
class NonStationaryShapeTemporalModel(AbstractTemporalLinearMarginModel):
- def load_margin_functions(self, param_name_to_dims=None):
- super().load_margin_functions({GevParams.SHAPE: [self.coordinates.idx_temporal_coordinates]})
+ def load_margin_function(self, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.SHAPE: [self.coordinates.idx_temporal_coordinates]})
@property
def shl(self):
@@ -55,9 +55,9 @@ class NonStationaryShapeTemporalModel(AbstractTemporalLinearMarginModel):
class NonStationaryLocationAndScaleTemporalModel(AbstractTemporalLinearMarginModel):
- def load_margin_functions(self, param_name_to_dims=None):
- super().load_margin_functions({GevParams.LOC: [self.coordinates.idx_temporal_coordinates],
- GevParams.SCALE: [self.coordinates.idx_temporal_coordinates]})
+ def load_margin_function(self, param_name_to_dims=None):
+ return super().load_margin_function({GevParams.LOC: [self.coordinates.idx_temporal_coordinates],
+ GevParams.SCALE: [self.coordinates.idx_temporal_coordinates]})
@property
def mul(self):
diff --git a/extreme_fit/model/margin_model/parametric_margin_model.py b/extreme_fit/model/margin_model/parametric_margin_model.py
index 6aba0d05..04aa53e3 100644
--- a/extreme_fit/model/margin_model/parametric_margin_model.py
+++ b/extreme_fit/model/margin_model/parametric_margin_model.py
@@ -2,8 +2,10 @@ from abc import ABC
import numpy as np
import pandas as pd
+from cached_property import cached_property
from extreme_fit.distribution.gev.gev_params import GevParams
+from extreme_fit.function.margin_function.abstract_margin_function import AbstractMarginFunction
from extreme_fit.function.margin_function.parametric_margin_function import \
ParametricMarginFunction
from extreme_fit.model.margin_model.abstract_margin_model import AbstractMarginModel
@@ -22,11 +24,16 @@ class ParametricMarginModel(AbstractMarginModel, ABC):
"""
:param starting_point: starting coordinate for the temporal trend
"""
+ super().__init__(coordinates, params_user, params_class)
self.fit_method = fit_method
self.starting_point = starting_point
- self.margin_function = None # type: ParametricMarginFunction
self.drop_duplicates = True
- super().__init__(coordinates, params_user, params_class)
+
+ @cached_property
+ def margin_function(self) -> ParametricMarginFunction:
+ margin_function = super().margin_function
+ assert isinstance(margin_function, ParametricMarginFunction)
+ return margin_function
def fitmargin_from_maxima_gev(self, data: np.ndarray, df_coordinates_spat: pd.DataFrame,
df_coordinates_temp: pd.DataFrame) -> ResultFromSpatialExtreme:
diff --git a/extreme_fit/model/margin_model/spline_margin_model.py b/extreme_fit/model/margin_model/spline_margin_model.py
index 83addd3c..b5d97d22 100644
--- a/extreme_fit/model/margin_model/spline_margin_model.py
+++ b/extreme_fit/model/margin_model/spline_margin_model.py
@@ -15,10 +15,10 @@ class SplineMarginModel(ParametricMarginModel):
params_user=None):
super().__init__(coordinates, params_user)
- def load_margin_functions(self, param_name_to_dims: Dict[str, List[int]] = None,
- param_name_to_coef: Dict[str, AbstractCoef] = None,
- param_name_to_nb_knots: Dict[str, int] = None,
- degree=3):
+ def load_margin_function(self, param_name_to_dims: Dict[str, List[int]] = None,
+ param_name_to_coef: Dict[str, AbstractCoef] = None,
+ param_name_to_nb_knots: Dict[str, int] = None,
+ degree=3):
# Default parameters
# todo: for the default parameters: take inspiration from the linear_margin_model
# also implement the class method thing
@@ -36,27 +36,24 @@ class SplineMarginModel(ParametricMarginModel):
if param_name_to_nb_knots is None:
param_name_to_nb_knots = {param_name: 2 for param_name in GevParams.PARAM_NAMES}
- # Load sample coef
- 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)
+ return 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):
- def load_margin_functions(self, param_name_to_dims: Dict[str, List[int]] = None,
- param_name_to_coef: Dict[str, AbstractCoef] = None,
- param_name_to_nb_knots: Dict[str, int] = None, degree=3):
- super().load_margin_functions({}, param_name_to_coef, param_name_to_nb_knots,
- degree)
+ def load_margin_function(self, param_name_to_dims: Dict[str, List[int]] = None,
+ param_name_to_coef: Dict[str, AbstractCoef] = None,
+ param_name_to_nb_knots: Dict[str, int] = None, degree=3):
+ return super().load_margin_function({}, param_name_to_coef, param_name_to_nb_knots, degree)
class Degree1SplineMarginModel(SplineMarginModel):
- def load_margin_functions(self, param_name_to_dims: Dict[str, List[int]] = None,
- param_name_to_coef: Dict[str, AbstractCoef] = None,
- param_name_to_nb_knots: Dict[str, int] = None, degree=3):
- super().load_margin_functions(param_name_to_dims, param_name_to_coef, param_name_to_nb_knots,
- degree=1)
+ def load_margin_function(self, param_name_to_dims: Dict[str, List[int]] = None,
+ param_name_to_coef: Dict[str, AbstractCoef] = None,
+ param_name_to_nb_knots: Dict[str, int] = None, degree=3):
+ return super().load_margin_function(param_name_to_dims, param_name_to_coef, param_name_to_nb_knots, degree=1)
--
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