Source code for skcriteria.preprocessing._preprocessing_base
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# License: BSD-3 (https://tldrlegal.com/license/bsd-3-clause-license-(revised))
# Copyright (c) 2016-2021, Cabral, Juan; Luczywo, Nadia
# Copyright (c) 2022, 2023, 2024 QuatroPe
# All rights reserved.
# =============================================================================
# DOCS
# =============================================================================
"""Core functionalities to create transformers."""
# =============================================================================
# IMPORTS
# =============================================================================
import abc
from ..core import DecisionMatrix, SKCMethodABC
from ..utils import doc_inherit
# =============================================================================
# SKCTransformer ABC
# =============================================================================
[docs]
class SKCTransformerABC(SKCMethodABC):
"""Abstract class for all transformer in scikit-criteria."""
_skcriteria_dm_type = "transformer"
_skcriteria_abstract_class = True
@abc.abstractmethod
def _transform_data(self, **kwargs):
"""Apply the transformation logic to the decision matrix parameters.
Parameters
----------
kwargs:
The decision matrix as separated parameters.
Returns
-------
:py:class:`dict`
A dictionary with all the values of the decision matrix
transformed.
"""
raise NotImplementedError()
[docs]
def transform(self, dm):
"""Perform transformation on `dm`.
Parameters
----------
dm: :py:class:`skcriteria.data.DecisionMatrix`
The decision matrix to transform.
Returns
-------
:py:class:`skcriteria.data.DecisionMatrix`
Transformed decision matrix.
"""
data = dm.to_dict()
transformed_data = self._transform_data(**data)
transformed_dm = DecisionMatrix.from_mcda_data(**transformed_data)
return transformed_dm
# =============================================================================
# MATRIX & WEIGHTS TRANSFORMER
# =============================================================================
[docs]
class SKCMatrixAndWeightTransformerABC(SKCTransformerABC):
"""Transform weights and matrix together or independently.
The Transformer that implements this abstract class can be configured to
transform
`weights`, `matrix` or `both` so only that part of the DecisionMatrix
is altered.
This abstract class require to redefine ``_transform_weights`` and
``_transform_matrix``, instead of ``_transform_data``.
"""
_skcriteria_abstract_class = True
_skcriteria_parameters = ["target"]
_TARGET_WEIGHTS = "weights"
_TARGET_MATRIX = "matrix"
_TARGET_BOTH = "both"
def __init__(self, target):
if target not in (
self._TARGET_MATRIX,
self._TARGET_WEIGHTS,
self._TARGET_BOTH,
):
raise ValueError(
f"'target' can only be '{self._TARGET_WEIGHTS}', "
f"'{self._TARGET_MATRIX}' or '{self._TARGET_BOTH}', "
f"found '{target}'"
)
self._target = target
@property
def target(self):
"""Determine which part of the DecisionMatrix will be transformed."""
return self._target
@abc.abstractmethod
def _transform_weights(self, weights):
"""Execute the transform method over the weights.
Parameters
----------
weights: :py:class:`numpy.ndarray`
The weights to transform.
Returns
-------
:py:class:`numpy.ndarray`
The transformed weights.
"""
raise NotImplementedError()
@abc.abstractmethod
def _transform_matrix(self, matrix):
"""Execute the transform method over the matrix.
Parameters
----------
matrix: :py:class:`numpy.ndarray`
The decision matrix to transform
Returns
-------
:py:class:`numpy.ndarray`
The transformed matrix.
"""
raise NotImplementedError()
@doc_inherit(SKCTransformerABC._transform_data)
def _transform_data(self, matrix, weights, **kwargs):
transformed_mtx = matrix
transformed_weights = weights
if self._target in (self._TARGET_MATRIX, self._TARGET_BOTH):
transformed_mtx = self._transform_matrix(matrix)
if self._target in (self._TARGET_WEIGHTS, self._TARGET_BOTH):
transformed_weights = self._transform_weights(weights)
kwargs.update(
matrix=transformed_mtx, weights=transformed_weights, dtypes=None
)
return kwargs