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

import logging

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

from gemseo.mlearning.transform.transformer import Transformer
from gemseo.mlearning.transform.transformer import TransformerFitOptionType
from gemseo.utils.python_compatibility import Final

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)

[docs]    def fit(self, data: ndarray, *args: TransformerFitOptionType) -> None:
if data.ndim == 1:
data = data[:, None]

super().fit(data, *args)

def _fit(self, data: ndarray, *args: TransformerFitOptionType) -> None:
n_features = data.shape[1]
coefficient = self.parameters[self.__COEFFICIENT]
if coefficient.size == 1 and n_features > 1:
self.parameters[self.__COEFFICIENT] = full(n_features, coefficient[0])

offset = self.parameters[self.__OFFSET]
if offset.size == 1 and n_features > 1:
self.parameters[self.__OFFSET] = full(n_features, offset[0])

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:
return diag(self.coefficient)

[docs]    def compute_jacobian_inverse(self, data: ndarray) -> ndarray:
return diag(1 / self.coefficient)