from extreme_estimator.estimator.abstract_estimator import AbstractEstimator from extreme_estimator.R_fit.max_stable_fit.abstract_max_stable_model import AbstractMaxStableModel from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset import numpy as np class MaxStableEstimator(AbstractEstimator): MAE_ERROR = 'mae' def __init__(self, dataset: AbstractDataset, max_stable_model: AbstractMaxStableModel): self.dataset = dataset self.max_stable_model = max_stable_model # Fit parameters self.max_stable_params_fitted = None def fit(self): self.max_stable_params_fitted = self.max_stable_model.fitmaxstab(maxima=self.dataset.maxima, coord=self.dataset.coord) 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}}