"""Module for the Nsb mutual information estimator."""
from abc import ABC
from numpy import issubdtype, integer
from infomeasure.estimators.base import (
MutualInformationEstimator,
ConditionalMutualInformationEstimator,
)
from ..entropy.nsb import NsbEntropyEstimator
from infomeasure import Config
from infomeasure.utils.types import LogBaseType
[docs]
class BaseNsbMIEstimator(ABC):
r"""Base class for Nsb 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.
K : int, optional
The support size. If not provided, uses the observed support size.
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,
K: int = None,
offset: int = 0,
base: LogBaseType = Config.get("base"),
**kwargs,
):
r"""Initialize the Nsb 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.
K : int, optional
The support size. If not provided, uses the observed support size.
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,
)
if K is not None and (not isinstance(K, int) or K <= 0):
raise ValueError("The K parameter must be a positive integer.")
self.K = K
[docs]
class NsbMIEstimator(BaseNsbMIEstimator, MutualInformationEstimator):
r"""Estimator for the Nsb mutual information.
Nsb 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.
K : int, optional
The support size. If not provided, uses the observed support size.
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 Nsb 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.nsb.NsbEntropyEstimator
Nsb entropy estimator.
"""
def _calculate(self):
"""Calculate the mutual information of the data.
Returns
-------
float
Nsb mutual information of the data.
"""
return self._generic_mi_from_entropy(
estimator=NsbEntropyEstimator,
kwargs={"K": self.K, "base": self.base},
)
[docs]
class NsbCMIEstimator(BaseNsbMIEstimator, ConditionalMutualInformationEstimator):
r"""Estimator for the conditional Nsb mutual information.
Nsb 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.
K : int, optional
The support size. If not provided, uses the observed support size.
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 Nsb 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.nsb.NsbEntropyEstimator
Nsb entropy estimator.
"""
def _calculate(self):
"""Calculate the conditional mutual information of the data.
Returns
-------
float
Conditional Nsb mutual information of the data.
"""
return self._generic_cmi_from_entropy(
estimator=NsbEntropyEstimator,
kwargs={"K": self.K, "base": self.base},
)
