Source code for gemseo_calibration.measure

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
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"""A module to measure the consistency or the inconsistency between two data sets."""
from __future__ import annotations

from typing import ClassVar

from gemseo.core.dataset import Dataset
from gemseo.core.mdofunctions.mdo_function import MDOFunction
from numpy import infty
from numpy import nanmax
from numpy import nanmin
from numpy import ndarray


[docs]class CalibrationMeasure(MDOFunction): """A measure of the consistency (or inconsistency) between two data sets.""" output_name: str """The name of the output used by the measure for calibration.""" maximize: ClassVar[bool] = False """Whether to maximize the calibration measure.""" def __init__( self, output_name: str, name: str | None = None, f_type: str | None = None ) -> None: # noqa: D205,D212,D415 """ Args: output_name: The name of the output to be taken into account by the measure. """ self.output_name = output_name super().__init__(None, name or self._compute_name(), f_type=f_type) self._lower_bound = -infty self._upper_bound = infty self._reference_data = [] @property def full_output_name(self) -> str: """The full name of the output.""" return self.output_name def _compute_name(self) -> str: """Return the name of the measure.""" return f"{self.__class__.__name__}({self.output_name})"
[docs] def set_reference_data(self, reference_dataset: Dataset) -> None: """Define the reference input-output data set. Args: reference_dataset: The reference input-output data set. """ self._reference_data = reference_dataset[self.output_name] self._lower_bound = nanmin(self._reference_data) self._upper_bound = nanmax(self._reference_data)
def _update_bounds(self, data: ndarray) -> None: """Update the lower and upper bounds of the output. Args: data: The value of the output. """ self._lower_bound = min(data.min(), self._lower_bound) self._upper_bound = max(data.max(), self._upper_bound) def __call__(self, model_dataset: Dataset) -> float: """Measure the (in)consistency between the model dataset and the reference one. Args: model_dataset: The model dataset. Returns: The measure of the (in)consistency between the model and reference datasets. """ raise NotImplementedError @staticmethod def _compare_data( data: ndarray, other_data: ndarray, ) -> ndarray: """Compare two data arrays. Args: data: The first data array. other_data: The second data array. Returns: The comparison between the two data arrays. """ raise NotImplementedError