Source code for gemseo_umdo.estimators.sampling

# 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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"""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.base_factory import BaseFactory

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(BaseFactory): """The factory of sampling-based statistic estimators.""" _CLASS = SamplingEstimator _MODULE_NAMES = ()
[docs]class Mean(SamplingEstimator): """Estimator of the expectation, a.k.a. mean. """ def __call__(self, samples: ndarray, **kwargs: Any) -> float | ndarray: """ Args: samples: The output evaluations arranged in rows. """ # noqa: D205 D212 D415 return samples.mean(0)
[docs]class Variance(SamplingEstimator): """Estimator of the variance.""" def __call__(self, samples: ndarray, **kwargs: Any) -> float | ndarray: """ Args: samples: The output evaluations arranged in rows. """ # noqa: D205 D212 D415 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: """ 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. """ # noqa: D205 D212 D415 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: Any) -> float | ndarray: """ Args: samples: The output evaluations arranged in rows. """ # noqa: D205 D212 D415 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: Any, ) -> float | ndarray: """ Args: samples: The output evaluations arranged in rows. factor: The factor related to the standard deviation. """ # noqa: D205 D212 D415 return self.__mean(samples, **kwargs) + factor * self.__standard_deviation( samples, **kwargs )