Source code for gemseo.mlearning.transformers.scaler.scaler

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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.
# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                         documentation
#        :author: Matthias De Lozzo, Syver Doving Agdestein
"""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.
the scaling operation is the identity: :math:`\\bar{z}=z`.
This method has to be overloaded.

.. seealso::

from __future__ import annotations

import logging
from typing import Final

from numpy import atleast_1d
from numpy import diag
from numpy import full
from numpy import ndarray
from numpy import tile

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

LOGGER = logging.getLogger(__name__)

[docs]class Scaler(Transformer): """Data scaler.""" __OFFSET: Final[str] = "offset" __COEFFICIENT: Final[str] = "coefficient" def __init__( self, name: str = "Scaler", offset: float | ndarray = 0.0, coefficient: float | ndarray = 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) self.offset = offset self.coefficient = coefficient @property def offset(self) -> ndarray: """The scaling offset.""" return self.parameters[self.__OFFSET] @property def coefficient(self) -> ndarray: """The scaling coefficient.""" return self.parameters[self.__COEFFICIENT] @offset.setter def offset(self, value: float | ndarray) -> None: self.parameters[self.__OFFSET] = atleast_1d(value) @coefficient.setter def coefficient(self, value: float | ndarray) -> None: self.parameters[self.__COEFFICIENT] = atleast_1d(value) def _fit(self, data: ndarray, *args: TransformerFitOptionType) -> None: if self.parameters[self.__COEFFICIENT].size == 1: self.parameters[self.__COEFFICIENT] = full( data.shape[-1], self.parameters[self.__COEFFICIENT][0] ) if self.parameters[self.__OFFSET].size == 1: self.parameters[self.__OFFSET] = full( data.shape[-1], self.parameters[self.__OFFSET][0] ) LOGGER.warning( ( "The function does nothing; " "the instance of %s uses the coefficient and offset " "passed at its initialization" ), self.__class__.__name__, self.__class__.__name__, ) @Transformer._use_2d_array def transform(self, data: ndarray) -> ndarray: return data @ diag(self.coefficient) + self.offset @Transformer._use_2d_array def inverse_transform(self, data: ndarray) -> ndarray: return (data - self.offset) @ diag(1 / self.coefficient) @Transformer._use_2d_array def compute_jacobian(self, data: ndarray) -> ndarray: return tile(diag(self.coefficient), (len(data), 1, 1)) @Transformer._use_2d_array def compute_jacobian_inverse(self, data: ndarray) -> ndarray: return tile(diag(1 / self.coefficient), (len(data), 1, 1))