Source code for gemseo_umdo.estimators.sampling

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
# Copyright 2022 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
"""Estimators of statistic for sampling-based U-MDO formulation."""
from __future__ import annotations

from typing import Any
from typing import TYPE_CHECKING

from gemseo.core.factory import Factory

if TYPE_CHECKING:
    from gemseo_umdo.formulations.sampling import Sampling

from numpy import ndarray

from gemseo_umdo.estimators.estimator import BaseStatisticEstimator


[docs]class SamplingEstimator(BaseStatisticEstimator): """Base statistic estimator for a U-MDO formulation using sampling.""" def __init__(self, formulation: Sampling) -> None: # noqa: D107 super().__init__(formulation)
[docs]class SamplingEstimatorFactory(Factory): """The factory of :class:`.SamplingEstimator`.""" def __init__(self) -> None: # noqa: D107 super().__init__(SamplingEstimator)
[docs]class Mean(SamplingEstimator): """Estimator of the expectation, a.k.a. mean. """ def __call__(self, samples: ndarray, **kwargs: Any) -> float | ndarray: """# noqa: D205 D212 D415 Args: samples: The output evaluations arranged in rows. """ return samples.mean(0)
[docs]class Variance(SamplingEstimator): """Estimator of the variance.""" def __call__(self, samples: ndarray, **kwargs) -> float | ndarray: """# noqa: D205 D212 D415 Args: samples: The output evaluations arranged in rows. """ return samples.var(0)
[docs]class Probability(SamplingEstimator): """Estimator of a probability.""" def __call__( self, samples: ndarray, threshold: float, greater: bool = True, **kwargs: Any, ) -> float | ndarray: """# noqa: D205 D212 D415 Args: samples: The output evaluations arranged in rows. threshold: The threshold against which the probability is estimated. greater: Whether to compute the probability of exceeding the threshold. """ if greater: return (samples >= threshold).mean(0) else: return (samples <= threshold).mean(0)
[docs]class StandardDeviation(Variance): """Estimator of the standard deviation.""" def __call__(self, samples: ndarray, **kwargs) -> float | ndarray: """# noqa: D205 D212 D415 Args: samples: The output evaluations arranged in rows. """ return super().__call__(samples, **kwargs) ** 0.5
[docs]class Margin(SamplingEstimator): """Estimator of a margin, i.e. mean + factor * deviation.""" def __init__(self, formulation: Sampling) -> None: # noqa: D107 super().__init__(formulation) self.__mean = Mean(formulation) self.__standard_deviation = StandardDeviation(formulation) def __call__( self, samples: ndarray, factor: float = 2.0, **kwargs, ) -> float | ndarray: """# noqa: D205 D212 D415 Args: samples: The output evaluations arranged in rows. factor: The factor related to the standard deviation. """ return self.__mean(samples, **kwargs) + factor * self.__standard_deviation( samples, **kwargs )