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

# 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
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# This program is distributed in the hope that it will be useful,
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# 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 geometrical linear transformation.

The :class:.MinMaxScaler class implements the MinMax scaling method
applying to some parameter :math:z:

.. math::

\\bar{z} := \\text{offset} + \\text{coefficient}\\times z
= \\frac{z-\\text{min}(z)}{(\\text{max}(z)-\\text{min}(z))},

where :math:\\text{offset}=-\\text{min}(z)/(\\text{max}(z)-\\text{min}(z))
and :math:\\text{coefficient}=1/(\\text{max}(z)-\\text{min}(z)).

In the MinMax scaling method,
the scaling operation linearly transforms the original variable :math:z
such that the minimum of the original data corresponds to 0 and the maximum to 1.
"""
from __future__ import annotations

from numpy import ndarray

from gemseo.mlearning.transform.scaler.scaler import Scaler
from gemseo.mlearning.transform.transformer import TransformerFitOptionType

[docs]class MinMaxScaler(Scaler):
"""Min-max scaler."""

def __init__(
self,
name: str = "MinMaxScaler",
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, coefficient)

def _fit(
self,
data: ndarray,
*args: TransformerFitOptionType,
) -> None:
l_b = data.min(0)
u_b = data.max(0)
self.offset = -l_b / (u_b - l_b)
self.coefficient = 1 / (u_b - l_b)