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Le Roux Erwan authored
[EXTREME ESTIMATOR][MARGIN MODEL] add temporal linear margin model test. check that starting point are taken into account.
7f777092
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import time
from extreme_estimator.extreme_models.margin_model.linear_margin_model import LinearMarginModel
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.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.param_function.linear_coef import LinearCoef
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
class AbstractEstimator(object):
DURATION = 'Average duration'
MAE_ERROR = 'Mean Average Error'
# For each estimator, we shall define:
# - The loss
# - The optimization method for each part of the process
def __init__(self, dataset: AbstractDataset):
self.dataset = dataset # type: AbstractDataset
self.additional_information = dict()
self._result_from_fit = None # type: ResultFromFit
self._margin_function_fitted = None
self._max_stable_model_fitted = None
@classmethod
def from_dataset(cls, dataset: AbstractDataset):
# raise NotImplementedError('from_dataset class constructor has not been implemented for this class')
pass
def fit(self):
ts = time.time()
self._fit()
te = time.time()
self.additional_information[self.DURATION] = int((te - ts) * 1000)
@property
def result_from_fit(self) -> ResultFromFit:
assert self._result_from_fit is not None, 'Fit has not be done'
return self._result_from_fit
@property
def margin_function_fitted(self) -> AbstractMarginFunction:
if self._margin_function_fitted is None:
self._margin_function_fitted = self.extract_function_fitted()
assert self._margin_function_fitted is not None, 'No margin function has been fitted'
return self._margin_function_fitted
def extract_function_fitted(self) -> AbstractMarginFunction:
raise NotImplementedError
def extract_function_fitted_from_the_model_shape(self, margin_model: LinearMarginModel):
return LinearMarginFunction.from_coef_dict(coordinates=self.dataset.coordinates,
gev_param_name_to_dims=margin_model.margin_function_start_fit.gev_param_name_to_dims,
coef_dict=self.result_from_fit.margin_coef_dict,
starting_point=margin_model.starting_point)
# @property
# def max_stable_fitted(self) -> AbstractMarginFunction:
# assert self._margin_function_fitted is not None, 'Error: estimator has not been fitted'
# return self._margin_function_fitted
@property
def train_split(self):
return self.dataset.train_split
# Methods to override in the child class
def _fit(self):
raise NotImplementedError