Source code for infomeasure.estimators.mutual_information.chao_shen

"""Module for the ChaoShen mutual information estimator."""

from abc import ABC

from numpy import issubdtype, integer

from infomeasure.estimators.base import (
    MutualInformationEstimator,
    ConditionalMutualInformationEstimator,
)

from ..entropy.chao_shen import ChaoShenEntropyEstimator
from infomeasure import Config
from infomeasure.utils.types import LogBaseType




class BaseChaoShenMIEstimator(ABC):
    r"""Base class for ChaoShen mutual information estimators.

    Attributes
    ----------
    *data : array-like, shape (n_samples,)
        The data used to estimate the (conditional) mutual information.
        You can pass an arbitrary number of data arrays as positional arguments.
        For conditional mutual information, only two data arrays are allowed.
    cond : array-like, optional
        The conditional data used to estimate the conditional mutual information.

    offset : int, optional
        Number of positions to shift the data arrays relative to each other.
        Delay/lag/shift between the variables. Default is no shift.
        Not compatible with the ``cond`` parameter / conditional MI.
    """

    def __init__(
        self,
        *data,
        cond=None,
        offset: int = 0,
        base: LogBaseType = Config.get("base"),
        **kwargs,
    ):
        r"""Initialize the ChaoShen estimator with specific parameters.

        Parameters
        ----------
        *data : array-like, shape (n_samples,)
            The data used to estimate the (conditional) mutual information.
            You can pass an arbitrary number of data arrays as positional arguments.
            For conditional mutual information, only two data arrays are allowed.
        cond : array-like, optional
            The conditional data used to estimate the conditional mutual information.

        offset : int, optional
            Number of positions to shift the X and Y data arrays relative to each other.
            Delay/lag/shift between the variables. Default is no shift.
            Not compatible with the ``cond`` parameter / conditional MI.
        """
        if cond is None:
            super().__init__(*data, offset=offset, base=base, **kwargs)
        else:
            super().__init__(
                *data,
                cond=cond,
                offset=offset,
                base=base,
                **kwargs,
            )





class ChaoShenMIEstimator(BaseChaoShenMIEstimator, MutualInformationEstimator):
    r"""Estimator for the ChaoShen mutual information.

    ChaoShen mutual information estimator using the entropy combination formula.

    Attributes
    ----------
    *data : array-like, shape (n_samples,)
        The data used to estimate the mutual information.
        You can pass an arbitrary number of data arrays as positional arguments.

    offset : int, optional
        Number of positions to shift the data arrays relative to each other.
        Delay/lag/shift between the variables. Default is no shift.

    Notes
    -----
    This estimator uses the ChaoShen entropy estimator to compute mutual
    information through the entropy combination formula.

    Note that the entropy combination formula is used (_generic_mi_from_entropy)
    not a dedicated implementation as other MI might have.

    See Also
    --------
    infomeasure.estimators.entropy.chao_shen.ChaoShenEntropyEstimator
        ChaoShen entropy estimator.
    """

    def _calculate(self):
        """Calculate the mutual information of the data.

        Returns
        -------
        float
            ChaoShen mutual information of the data.
        """

        return self._generic_mi_from_entropy(
            estimator=ChaoShenEntropyEstimator,
            kwargs={"base": self.base},
        )





class ChaoShenCMIEstimator(
    BaseChaoShenMIEstimator, ConditionalMutualInformationEstimator
):
    r"""Estimator for the conditional ChaoShen mutual information.

    ChaoShen conditional mutual information estimator using the entropy combination formula.

    Attributes
    ----------
    *data : array-like, shape (n_samples,)
        The data used to estimate the conditional mutual information.
        You can pass an arbitrary number of data arrays as positional arguments.
    cond : array-like
        The conditional data used to estimate the conditional mutual information.

    offset : int, optional
        Number of positions to shift the data arrays relative to each other.
        Delay/lag/shift between the variables. Default is no shift.

    Notes
    -----
    This estimator uses the ChaoShen entropy estimator to compute conditional
    mutual information through the entropy combination formula.

    Note that the entropy combination formula is used (_generic_cmi_from_entropy)
    not a dedicated implementation as other MI might have.

    See Also
    --------
    infomeasure.estimators.entropy.chao_shen.ChaoShenEntropyEstimator
        ChaoShen entropy estimator.
    """

    def _calculate(self):
        """Calculate the conditional mutual information of the data.

        Returns
        -------
        float
            Conditional ChaoShen mutual information of the data.
        """
        return self._generic_cmi_from_entropy(
            estimator=ChaoShenEntropyEstimator,
            kwargs={"base": self.base},
        )