Source code for gemseo.mlearning.transform.scaler.scaler

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
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# This program is free software; you can redistribute it and/or
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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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# 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.
# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                         documentation
#        :author: Matthias De Lozzo, Syver Doving Agdestein
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""Scaling a variable with a linear transformation.

The :class:`.Scaler` class implements the default scaling method
applying to some parameter :math:`z`:

.. math::

    \\bar{z} := \\text{offset} + \\text{coefficient}\\times z

where :math:`\\bar{z}` is the scaled version of :math:`z`.
This scaling method is a linear transformation
parameterized by an offset and a coefficient.

In this default scaling method,
the offset is equal to 0 and the coefficient is equal to 1.
Consequently,
the scaling operation is the identity: :math:`\\bar{z}=z`.
This method has to be overloaded.

.. seealso::

   :mod:`~gemseo.mlearning.transform.scaler.min_max_scaler`
   :mod:`~gemseo.mlearning.transform.scaler.standard_scaler`
"""
from __future__ import annotations

import logging

from numpy import diag
from numpy import eye
from numpy import ndarray

from gemseo.mlearning.transform.transformer import Transformer
from gemseo.mlearning.transform.transformer import TransformerFitOptionType

LOGGER = logging.getLogger(__name__)


[docs]class Scaler(Transformer): """Data scaler.""" def __init__( self, name: str = "Scaler", offset: float = 0.0, coefficient: float = 1.0, ) -> None: """ Args: name: A name for this transformer. offset: The offset of the linear transformation. coefficient: The coefficient of the linear transformation. """ super().__init__(name, offset=offset, coefficient=coefficient) @property def offset(self) -> float: """The scaling offset.""" return self.parameters["offset"] @property def coefficient(self) -> float: """The scaling coefficient.""" return self.parameters["coefficient"] @offset.setter def offset( self, value: float, ) -> None: self.parameters["offset"] = value @coefficient.setter def coefficient( self, value: float, ) -> None: self.parameters["coefficient"] = value def _fit( self, data: ndarray, *args: TransformerFitOptionType, ) -> None: LOGGER.warning( ( "The %s.fit() function does nothing; " "the instance of %s uses the coefficient and offset " "passed at its initialization" ), self.__class__.__name__, self.__class__.__name__, )
[docs] def transform( self, data: ndarray, ) -> ndarray: return self.offset + self.coefficient * data
[docs] def inverse_transform( self, data: ndarray, ) -> ndarray: return (data - self.offset) / self.coefficient
[docs] def compute_jacobian( self, data: ndarray, ) -> ndarray: if not isinstance(self.coefficient, ndarray): return self.coefficient * eye(data.shape[-1]) else: return diag(self.coefficient)
[docs] def compute_jacobian_inverse( self, data: ndarray, ) -> ndarray: if not isinstance(self.coefficient, ndarray): return 1 / self.coefficient * eye(data.shape[-1]) else: return diag(1 / self.coefficient)