MMPC implementation

[kenneth-lee-ch]

I have looked at the MMPC documentation. How can I specify the target node for the MMPC? As far as I understand, MMPC is applied to a specific target and learn the parents and children of that node. I don't see any argument that takes the particular target node for MMPC.

The documentation shows the following example:

class MMPC(BNlearnAlgorithm):
    """Max-Min Parents-Children algorithm.

    **Description:** The Max-Min Parents-Children (MMPC) is a 2-phase algorithm
    with a forward pass and a backward pass. The forward phase adds recursively
    the variables that possess the highest association with the target
    conditionally to the already selected variables. The backward pass tests
    d-separability of variables conditionally to the set and subsets of the
    selected variables.

    **Required R packages**: bnlearn

    **Data Type:** Depends on the test used. Check
    :ref:`here <bnlearntests>` for the list of available tests.

    **Assumptions:** MMPC outputs markov blankets of nodes, with additional
    assumptions depending on the conditional test used.

    .. note::
       Tsamardinos I, Aliferis CF, Statnikov A (2003). "Time and Sample Efficient Discovery of Markov
       Blankets and Direct Causal Relations".  In "KDD ’03:  Proceedings of the Ninth ACM SIGKDD
       International Conference on Knowledge Discovery and Data Mining", pp. 673-678. ACM.
       Tsamardinos I, Brown LE, Aliferis CF (2006).  "The Max-Min Hill-Climbing Bayesian Network
       Structure Learning Algorithm".
       Machine Learning,65(1), 31-78.

    Example:
        >>> import networkx as nx
        >>> from cdt.causality.graph import MMPC
        >>> from cdt.data import load_dataset
        >>> data, graph = load_dataset("sachs")
        >>> obj = MMPC()
        >>> #The predict() method works without a graph, or with a
        >>> #directed or undirected graph provided as an input
        >>> output = obj.predict(data)    #No graph provided as an argument
        >>>
        >>> output = obj.predict(data, nx.Graph(graph))  #With an undirected graph
        >>>
        >>> output = obj.predict(data, graph)  #With a directed graph
        >>>
        >>> #To view the graph created, run the below commands:
        >>> nx.draw_networkx(output, font_size=8)
        >>> plt.show()
    """