Source code for gemseo.mlearning.transform.scaler.scaler
# -*- coding: utf-8 -*-
# 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.
# 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 division, unicode_literals
import logging
from numpy import diag, eye, ndarray
from gemseo.mlearning.transform.transformer import Transformer, TransformerFitOptionType
LOGGER = logging.getLogger(__name__)
[docs]class Scaler(Transformer):
"""Data scaler."""
def __init__(
self,
name="Scaler", # type: str
offset=0.0, # type: float
coefficient=1.0, # type: float
): # type: (...) -> None
"""
Args:
name: A name for this transformer.
offset: The offset of the linear transformation.
coefficient: The coefficient of the linear transformation.
"""
super(Scaler, self).__init__(name, offset=offset, coefficient=coefficient)
@property
def offset(self): # type: (...) -> float
"""The scaling offset."""
return self.parameters["offset"]
@property
def coefficient(self): # type: (...) -> float
"""The scaling coefficient."""
return self.parameters["coefficient"]
@offset.setter
def offset(
self,
value, # type: float
): # type: (...) -> None
self.parameters["offset"] = value
@coefficient.setter
def coefficient(
self,
value, # type: float
): # type: (...) -> None
self.parameters["coefficient"] = value
[docs] def fit(
self,
data, # type: ndarray
*args # type: TransformerFitOptionType
): # type: (...) -> 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 compute_jacobian(
self,
data, # type: ndarray
): # type: (...) -> 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, # type: ndarray
): # type: (...) -> ndarray
if not isinstance(self.coefficient, ndarray):
return 1 / self.coefficient * eye(data.shape[-1])
else:
return diag(1 / self.coefficient)
