grnet.abstract package

Module contents

class grnet.abstract.Estimator(data: DataFrame, n: int | None = None, random_state: int = 0)

Bases: object

Abstract class for wrapper classes of pgmpy.estimators

Methods

__init__(: self, data: pandas.DataFrame, n: int, random_state: int
) -> None:: initialize attributes
estimate(: self, **kwargs
) -> None:: estimate network and save edges (a list of tuples) as self.edges
get_matrix(: self
) -> pandas.DataFrame:: export network information as DxD matrix of 0 or 1 elements

Attributes

data: pandas.DataFrame: input data or resampled data (data will be resampled if n is specified in self.__init__)
edges: List[tuple]: information of edges are saved as a list of tuples after self.estimate was run

estimate(**kwargs) → None

Parameters

**kwargs: kwargs for corresponding classes of pgmpy.estimators

Returns

None

get_matrix() → DataFrame

Parameters

None

Returns

GRNMatrix: pandas.DataFrame: edge information of the GRN will be returned as a DxD matrix