max_stable_estimator.py

from extreme_estimator.estimator.abstract_estimator import AbstractEstimator
from extreme_estimator.extreme_models.max_stable_model.abstract_max_stable_model import AbstractMaxStableModel
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
import numpy as np

from spatio_temporal_dataset.dataset.spatio_temporal_split import SpatialTemporalSplit


class AbstractMaxStableEstimator(AbstractEstimator):

    def __init__(self, dataset: AbstractDataset, max_stable_model: AbstractMaxStableModel):
        super().__init__(dataset=dataset)
        self.max_stable_model = max_stable_model
        # Fit parameters
        self.max_stable_params_fitted = None


class MaxStableEstimator(AbstractMaxStableEstimator):

    def _fit(self):
        assert self.dataset.maxima_frech(split=SpatialTemporalSplit.train) is not None
        self.max_stable_params_fitted = 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))

    def _error(self, true_max_stable_params: dict):
        absolute_errors = {param_name: np.abs(param_true_value - self.max_stable_params_fitted[param_name])
                           for param_name, param_true_value in true_max_stable_params.items()}
        mean_absolute_error = np.mean(np.array(list(absolute_errors.values())))
        return {**absolute_errors, **{self.MAE_ERROR: mean_absolute_error}}