BaseEliminationOrder

class pgmpy.inference.EliminationOrder.BaseEliminationOrder(model)

Bases: object

Init method for the base class of Elimination Orders.

Parameters:

model: DiscreteBayesianNetwork instance

The model on which we want to compute the elimination orders.

abstractmethod cost(node)

The cost function to compute the cost of elimination of each node. This method is just a dummy and returns 0 for all the nodes. Actual cost functions are implemented in the classes inheriting BaseEliminationOrder.

Parameters:

node: string, any hashable python object.

The node whose cost is to be computed.

fill_in_edges(node)

Return edges needed to be added to the graph if a node is removed.

Parameters:

node: string (any hashable python object)

Node to be removed from the graph.

get_elimination_order(nodes=None, show_progress=True)

Returns the optimal elimination order based on the cost function. The node having the least cost is removed first.

Parameters:

nodes: list, tuple, set (array-like)

The variables which are to be eliminated.

Examples

>>> import numpy as np
>>> from pgmpy.models import DiscreteBayesianNetwork
>>> from pgmpy.factors.discrete import TabularCPD
>>> from pgmpy.inference.EliminationOrder import WeightedMinFill
>>> rng = np.random.default_rng(42)
>>> model = DiscreteBayesianNetwork(
...     [
...         ("c", "d"),
...         ("d", "g"),
...         ("i", "g"),
...         ("i", "s"),
...         ("s", "j"),
...         ("g", "l"),
...         ("l", "j"),
...         ("j", "h"),
...         ("g", "h"),
...     ]
... )
>>> cpd_c = TabularCPD("c", 2, rng.random((2, 1)))
>>> cpd_d = TabularCPD("d", 2, rng.random((2, 2)), ["c"], [2])
>>> cpd_g = TabularCPD("g", 3, rng.random((3, 4)), ["d", "i"], [2, 2])
>>> cpd_i = TabularCPD("i", 2, rng.random((2, 1)))
>>> cpd_s = TabularCPD("s", 2, rng.random((2, 2)), ["i"], [2])
>>> cpd_j = TabularCPD("j", 2, rng.random((2, 4)), ["l", "s"], [2, 2])
>>> cpd_l = TabularCPD("l", 2, rng.random((2, 3)), ["g"], [3])
>>> cpd_h = TabularCPD("h", 2, rng.random((2, 6)), ["g", "j"], [3, 2])
>>> model.add_cpds(cpd_c, cpd_d, cpd_g, cpd_i, cpd_s, cpd_j, cpd_l, cpd_h)
>>> WeightedMinFill(model).get_elimination_order(["c", "d", "g", "l", "s"])
['c', 'd', 's', 'l', 'g']
>>> WeightedMinFill(model).get_elimination_order(["c", "d", "g", "l", "s"])
['c', 'd', 's', 'l', 'g']
>>> WeightedMinFill(model).get_elimination_order(["c", "d", "g", "l", "s"])
['c', 'd', 's', 'l', 'g']