<reponame>zarebulic/neural-semigroup-experiment<filename>neural_semigroups/associator_loss.py
"""
   Copyright 2019-2021 <NAME>

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
"""
import torch
from torch import Tensor
from torch.functional import einsum
from torch.nn import Module
from torch.nn.functional import kl_div

from neural_semigroups.utils import count_different, make_discrete


class AssociatorLoss(Module):
    """
    probabilistic associator loss
    """

    def __init__(self, discrete: bool = False):
        """
        :param discrete: when ``False``, the KL divergence is is used for measuring associativity in a continuous way.
            when ``True``, returns ``1`` for associative and ``0`` for non associative samples.
        """
        super().__init__()
        self.discrete = discrete

    def forward(self, cayley_cubes: Tensor) -> Tensor:
        """
        finds a probabilistic associator of a given probabilistic Cayley cube

        First, we build two 4-index tensors representing probability
        distributions of products :math:`a\\left(bc\\right)` and :math:`\\left(ab\\right)c`, respectively:

        :math:`T_{ijkl}=P\\left\\{e_i\\left(e_je_k\\right)=e_l\\right\\}=
        \\sum\\limits_{m=1}^nP\\left\\{e_ie_m=e_l\\vert e_je_k=e_m\\right\\}
        P\\left\\{e_je_k=e_m\\right\\}=\\sum\\limits_{m=1}^na_{iml}a_{jkm}`

        and

        :math:`T\\prime_{ijkl}=P\\left\\{\\left(e_ie_j\\right)e_k=e_l\\right\\}=
        \\sum\\limits_{m=1}^nP\\left\\{e_me_k=e_l\\vert e_ie_j=e_m\\right\\}
        P\\left\\{e_ie_j=e_m\\right\\}=\\sum\\limits_{m=1}^na_{mkl}a_{ijm}`

        Then we calculate `Kullback-Leibler divergence`_ between :math:`T_{ijkl}`
        and :math:`T\\prime_{ijkl}` to find a continuous measure of associativity of the input table.

        :param cayley_cubes: a batch of probabilistic Cayley cubes
        :returns: the probabilistic associator

        .. _Kullback-Leibler divergence: https://en.wikipedia.org/wiki/Kullback-Leibler_divergence

        """
        cubes = make_discrete(cayley_cubes) if self.discrete else cayley_cubes
        one = einsum("biml,bjkm->bijkl", cubes, cubes)
        two = einsum("bmkl,bijm->bijkl", cubes, cubes)
        batch_size = cayley_cubes.shape[0]
        associator = (
            count_different(one, two)
            if self.discrete
            else kl_div(torch.log(one), two, reduction="sum")
        )
        return associator / batch_size

"""
   Copyright 2019-2021 <NAME>

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
"""
import torch
from torch import Tensor
from torch.functional import einsum
from torch.nn import Module
from torch.nn.functional import kl_div

from neural_semigroups.utils import count_different, make_discrete


class AssociatorLoss(Module):
    """
    probabilistic associator loss
    """

    def __init__(self, discrete: bool = False):
        """
        :param discrete: when ``False``, the KL divergence is is used for measuring associativity in a continuous way.
            when ``True``, returns ``1`` for associative and ``0`` for non associative samples.
        """
        super().__init__()
        self.discrete = discrete

    def forward(self, cayley_cubes: Tensor) -> Tensor:
        """
        finds a probabilistic associator of a given probabilistic Cayley cube

        First, we build two 4-index tensors representing probability
        distributions of products :math:`a\\left(bc\\right)` and :math:`\\left(ab\\right)c`, respectively:

        :math:`T_{ijkl}=P\\left\\{e_i\\left(e_je_k\\right)=e_l\\right\\}=
        \\sum\\limits_{m=1}^nP\\left\\{e_ie_m=e_l\\vert e_je_k=e_m\\right\\}
        P\\left\\{e_je_k=e_m\\right\\}=\\sum\\limits_{m=1}^na_{iml}a_{jkm}`

        and

        :math:`T\\prime_{ijkl}=P\\left\\{\\left(e_ie_j\\right)e_k=e_l\\right\\}=
        \\sum\\limits_{m=1}^nP\\left\\{e_me_k=e_l\\vert e_ie_j=e_m\\right\\}
        P\\left\\{e_ie_j=e_m\\right\\}=\\sum\\limits_{m=1}^na_{mkl}a_{ijm}`

        Then we calculate `Kullback-Leibler divergence`_ between :math:`T_{ijkl}`
        and :math:`T\\prime_{ijkl}` to find a continuous measure of associativity of the input table.

        :param cayley_cubes: a batch of probabilistic Cayley cubes
        :returns: the probabilistic associator

        .. _Kullback-Leibler divergence: https://en.wikipedia.org/wiki/Kullback-Leibler_divergence

        """
        cubes = make_discrete(cayley_cubes) if self.discrete else cayley_cubes
        one = einsum("biml,bjkm->bijkl", cubes, cubes)
        two = einsum("bmkl,bijm->bijkl", cubes, cubes)
        batch_size = cayley_cubes.shape[0]
        associator = (
            count_different(one, two)
            if self.discrete
            else kl_div(torch.log(one), two, reduction="sum")
        )
        return associator / batch_size
Copyright 2019-2021 <NAME>     Licensed under the Apache License, Version 2.0 (the "License");    you may not use this file except in compliance with the License.    You may obtain a copy of the License at         http://www.apache.org/licenses/LICENSE-2.0     Unless required by applicable law or agreed to in writing, software    distributed under the License is distributed on an "AS IS" BASIS,    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.    See the License for the specific language governing permissions and    limitations under the License. probabilistic associator loss :param discrete: when ``False``, the KL divergence is is used for measuring associativity in a continuous way.             when ``True``, returns ``1`` for associative and ``0`` for non associative samples. finds a probabilistic associator of a given probabilistic Cayley cube          First, we build two 4-index tensors representing probability         distributions of products :math:`a\\left(bc\\right)` and :math:`\\left(ab\\right)c`, respectively:          :math:`T_{ijkl}=P\\left\\{e_i\\left(e_je_k\\right)=e_l\\right\\}=         \\sum\\limits_{m=1}^nP\\left\\{e_ie_m=e_l\\vert e_je_k=e_m\\right\\}         P\\left\\{e_je_k=e_m\\right\\}=\\sum\\limits_{m=1}^na_{iml}a_{jkm}`          and          :math:`T\\prime_{ijkl}=P\\left\\{\\left(e_ie_j\\right)e_k=e_l\\right\\}=         \\sum\\limits_{m=1}^nP\\left\\{e_me_k=e_l\\vert e_ie_j=e_m\\right\\}         P\\left\\{e_ie_j=e_m\\right\\}=\\sum\\limits_{m=1}^na_{mkl}a_{ijm}`          Then we calculate `Kullback-Leibler divergence`_ between :math:`T_{ijkl}`         and :math:`T\\prime_{ijkl}` to find a continuous measure of associativity of the input table.          :param cayley_cubes: a batch of probabilistic Cayley cubes         :returns: the probabilistic associator          .. _Kullback-Leibler divergence: https://en.wikipedia.org/wiki/Kullback-Leibler_divergence