Source code for gemseo.mlearning.transform.transformer

# -*- 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: Syver Doving Agdestein
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""A transformer to apply operations on NumPy arrays.

The abstract :class:`.Transformer` class implements the concept of a data transformer.
Inheriting classes shall implement the :meth:`.Transformer.fit`,
:meth:`.Transformer.transform`
and possibly :meth:`.Transformer.inverse_transform` methods.

.. seealso::

   :mod:`~gemseo.mlearning.transform.scaler.scaler`
   :mod:`~gemseo.mlearning.transform.dimension_reduction.dimension_reduction`
"""
from __future__ import division, unicode_literals

from typing import NoReturn, Optional, Union

import six
from custom_inherit import DocInheritMeta
from numpy import ndarray

TransformerFitOptionType = Union[float, int, str]


@six.add_metaclass(
    DocInheritMeta(
        abstract_base_class=True,
        style="google_with_merge",
    )
)
class Transformer(object):
    """Transformer baseclass.

    Attributes:
        name (str): The name of the transformer.
        parameters (str): The parameters of the transformer.
    """

    CROSSED = False

    def __init__(
        self,
        name="Transformer",  # type: str
        **parameters  # type: Optional[Union[float,int,str,bool]]
    ):  # type: (...) -> None
        """
        Args:
            name: A name for this transformer.
            **parameters: The parameters of the transformer.
        """
        self.name = name
        self.parameters = parameters

    def duplicate(self):  # type: (...) -> Transformer
        """Duplicate the current object.

        Returns:
            A deepcopy of the current instance.
        """
        return self.__class__(self.name, **self.parameters)

    def fit(
        self,
        data,  # type: ndarray
        *args  # type: TransformerFitOptionType
    ):  # type: (...) -> NoReturn
        """Fit the transformer to the data.

        Args:
            data: The data to be fitted.
        """
        raise NotImplementedError

    def transform(
        self,
        data,  # type: ndarray
    ):  # type: (...) -> NoReturn
        """Transform the data.

        Args:
            data: The data to be transformed.

        Returns:
            The transformed data.
        """
        raise NotImplementedError

    def inverse_transform(
        self,
        data,  # type: ndarray
    ):  # type: (...) -> NoReturn
        """Perform an inverse transform on the data.

        Args:
            data: The data to be inverse transformed.

        Returns:
            The inverse transformed data.
        """
        raise NotImplementedError

    def fit_transform(
        self,
        data,  # type: ndarray
        *args  # type: TransformerFitOptionType
    ):  # type: (...) -> ndarray
        """Fit the transformer to the data and transform the data.

        Args:
            data: The data to be transformed.

        Returns:
            The transformed data.
        """
        self.fit(data, *args)
        return self.transform(data)

    def compute_jacobian(
        self,
        data,  # type: ndarray
    ):  # type: (...) -> NoReturn
        """Compute Jacobian of transformer.transform().

        Args:
            data: The data where the Jacobian is to be computed.

        Returns:
            The Jacobian matrix.
        """
        raise NotImplementedError

    def compute_jacobian_inverse(
        self,
        data,  # type: ndarray
    ):  # type: (...) -> NoReturn
        """Compute Jacobian of the transformer.inverse_transform().

        Args:
            data: The data where the Jacobian is to be computed.

        Returns:
            The Jacobian matrix.
        """
        raise NotImplementedError

    def __str__(self):  # type: (...) -> str
        return self.__class__.__name__