Source code for pgmpy.structure_score.aic_cond_gauss
from pgmpy.structure_score.log_likelihood_cond_gauss import LogLikelihoodCondGauss
[docs]
class AICCondGauss(LogLikelihoodCondGauss):
r"""
AIC structure score for Bayesian networks with mixed discrete and continuous variables.
This score penalizes the conditional-Gaussian log-likelihood by the number of free parameters. The local score
computed by `local_score(variable, parents)` is
.. math::
\operatorname{AIC}(X_i, \Pi_i) = \ell(X_i, \Pi_i) - k_i,
where :math:`\ell(X_i, \Pi_i)` is the local conditional-Gaussian log-likelihood and :math:`k_i` is the number of
free parameters returned by `_get_num_parameters`.
Parameters
----------
data : pandas.DataFrame
DataFrame where columns may be discrete or continuous variables.
state_names : dict, optional
Dictionary mapping discrete variable names to their possible states.
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> from pgmpy.structure_score import AICCondGauss
>>> rng = np.random.default_rng(0)
>>> data = pd.DataFrame(
... {
... "A": rng.normal(size=100),
... "B": rng.integers(0, 2, size=100),
... "C": rng.normal(size=100),
... }
... )
>>> score = AICCondGauss(data)
>>> round(score.local_score("A", ("B", "C")), 3)
np.float64(-141.319)
Raises
------
ValueError
If the log-likelihood or parameter count cannot be computed for the given local configuration.
References
----------
.. [1] Andrews, B., Ramsey, J., & Cooper, G. F. (2018). Scoring Bayesian Networks of Mixed Variables. International
Journal of Data Science and Analytics, 6(1), 3-18. https://doi.org/10.1007/s41060-017-0085-7
"""
_tags = {
"name": "aic-cg",
"supported_datatype": "mixed",
"default_for": None,
"is_parameteric": False,
}
def __init__(self, data, state_names=None):
super().__init__(data, state_names=state_names)
def _local_score(self, variable: str, parents: tuple[str, ...]) -> float:
ll = self._log_likelihood(variable=variable, parents=parents)
k = self._get_num_parameters(variable=variable, parents=parents)
return ll - k
