DAG¶

Copying¶

`DAG.copy`()	Return a copy of the current DAG.
`DAG.rename_nodes`(name_map, VT])	Rename the nodes in this graph according to `name_map`.
`DAG.induced_subgraph`(nodes)	Return the induced subgraph over only `nodes`

`DAG.parents_of`(nodes, Set[Hashable]])	Return all nodes that are parents of the node or set of nodes `nodes`.
`DAG.children_of`(nodes, Set[Hashable]])	Return all nodes that are children of the node or set of nodes `nodes`.
`DAG.neighbors_of`(nodes, Set[Hashable]])	Return all nodes that are adjacent to the node or set of nodes `node`.
`DAG.markov_blanket_of`(node)	Return the Markov blanket of `node`, i.e., the parents of the node, its children, and the parents of its children.
`DAG.ancestors_of`(nodes)	Return the ancestors of `nodes`.
`DAG.descendants_of`(nodes, Set[Hashable]])	Return the descendants of `node`.
`DAG.indegree_of`(node)	Return the indegree of `node`.
`DAG.outdegree_of`(node)	Return the outdegree of `node`.
`DAG.incoming_arcs`(node)	Return all arcs with target `node`.
`DAG.outgoing_arcs`(node)	Return all arcs with source `node`.
`DAG.incident_arcs`(node)	Return all arcs with `node` as either source or target.

`DAG.add_node`(node)	Add `node` to the DAG.
`DAG.add_nodes_from`(nodes)	Add nodes to the graph from the collection `nodes`.
`DAG.remove_node`(node[, ignore_error])	Remove the node `node` from the graph.
`DAG.add_arc`(i, j[, check_acyclic])	Add the arc `i` -> `j` to the DAG
`DAG.add_arcs_from`(arcs[, check_acyclic])	Add arcs to the graph from the collection `arcs`.
`DAG.remove_arc`(i, j[, ignore_error])	Remove the arc `i` -> `j`.
`DAG.reverse_arc`(i, j[, ignore_error, …])	Reverse the arc `i` -> `j` to `i` <- `j`.

`DAG.has_arc`(source, target)	Check if this DAG has an arc `source` -> `target`.
`DAG.sources`()	Get all nodes in the graph that have no parents.
`DAG.sinks`()	Get all nodes in the graph that have no children.
`DAG.reversible_arcs`()	Get all reversible (aka covered) arcs in the DAG.
`DAG.is_reversible`(i, j)	Check if the arc `i` -> `j` is reversible (aka covered), i.e., if $pa(i) = pa(j) \setminus \{i\}$
`DAG.arcs_in_vstructures`()	Get all arcs in the graph that participate in a v-structure.
`DAG.vstructures`()	Get all v-structures in the graph, i.e., triples of the form (i, k, j) such that `i`->k<-`j` and `i` is not adjacent to `j`.
`DAG.triples`()	Return all triples of the form (`i`, `j`, `k`) such that `i` and `k` are both adjacent to `j`.
`DAG.upstream_most`(s)	Return the set of nodes which in `s` which have no ancestors in `s`.

`DAG.topological_sort`()	Return a topological sort of the nodes in the graph.
`DAG.is_topological`(order)	Check that `order` is a topological order consistent with this DAG, i.e., if `i`->``j`` in the DAG, then `i` comes before `j` in the order.
`DAG.permutation_score`(order)	Return the number of “errors” in `order` with respect to the DAG, i.e., the number of times that `i`->``j`` in the DAG but `i` comes after `j` in `order`.

`DAG.shd`(other)	Compute the structural Hamming distance between this DAG and the DAG `other`.
`DAG.shd_skeleton`(other)	Compute the structure Hamming distance between the skeleton of this DAG and the skeleton of the graph `other`.
`DAG.markov_equivalent`(other[, interventions])	Check if this DAG is (interventionally) Markov equivalent to the DAG `other`.
`DAG.is_imap`(other)	Check if this DAG is an IMAP of the DAG `other`, i.e., all d-separation statements in this graph are also d-separation statements in `other`.
`DAG.is_minimal_imap`(other[, certify, check_imap])	Check if this DAG is a minimal IMAP of other, i.e., it is an IMAP and no proper subgraph of this DAG is an IMAP of other.
`DAG.chickering_distance`(other)	Return the total number of edge reversals plus twice the number of edge additions/deletions required to turn this DAG into the DAG `other`.
`DAG.confusion_matrix`(other[, rates_only])	Return the “confusion matrix” associated with estimating the CPDAG of `other` instead of the CPDAG of this DAG.
`DAG.confusion_matrix_skeleton`(other)	Return the “confusion matrix” associated with estimating the skeleton of `other` instead of the skeleton of this DAG.

`DAG.dsep`(A, Hashable], B, Hashable], C, …)	Check if `A` and `B` are d-separated given `C`, using the Bayes ball algorithm.
`DAG.dsep_from_given`(A, C, …)	Find all nodes d-separated from `A` given `C`.
`DAG.is_invariant`(A, intervened_nodes[, …])	Check if the distribution of `A` given cond_set is invariant to an intervention on intervened_nodes.
`DAG.local_markov_statements`()	Return the local Markov statements of this DAG, i.e., those of the form `i` independent nondescendants(i) given the parents of `i`.

`DAG.from_amat`(amat)	Return a DAG with arcs given by `amat`, i.e.
`DAG.to_amat`([node_list])	Return an adjacency matrix for this DAG.
`DAG.from_nx`(nx_graph)	Convert a networkx DiGraph into a DAG.
`DAG.to_nx`()	Convert DAG to a networkx DiGraph.
`DAG.from_dataframe`(df)	Create a DAG from a dataframe, where the indices and columns are node names and a nonzero entry indicates the presence of an edge.
`DAG.to_dataframe`([node_list])	Turn this DAG into a dataframe, where the indices and columns are node names and a nonzero entry indicates the presence of an edge.

`DAG.moral_graph`()	Return the (undirected) moral graph of this DAG, i.e., the graph with the parents of all nodes made adjacent.
`DAG.marginal_mag`(latent_nodes[, relabel, new])	Return the maximal ancestral graph (MAG) that results from marginalizing out `latent_nodes`.
`DAG.cpdag`()	Return the completed partially directed acyclic graph (CPDAG, aka essential graph) that represents the Markov equivalence class of this DAG.
`DAG.interventional_cpdag`(interventions[, cpdag])	Return the interventional essential graph (aka CPDAG) associated with this DAG.

`DAG.resolved_sinks`(other)	Return the nodes in this graph which are “resolved sinks” with respect to the graph `other`.
`DAG.chickering_sequence`(imap[, verbose])	Return a Chickering sequence from this DAG to an I-MAP `imap`.
`DAG.apply_edge_operation`(imap[, seed_sink, …])	Identify an edge operation (covered edge reversal or edge addition) which decreases the Chickering distance from this DAG to `imap`.

`DAG.directed_clique_tree`([verbose])	Return the directed clique tree associated with this DAG.
`DAG.contracted_directed_clique_tree`()	Return the contracted directed clique tree associated with this DAG.
`DAG.residuals`()	Return the residuals associated with this DAG.
`DAG.residual_essential_graph`()	Return the residual essential graph associated with this DAG.

`DAG.optimal_fully_orienting_single_node_interventions`([…])	Find the smallest set of interventions which fully orients the CPDAG into this DAG.
`DAG.greedy_optimal_single_node_intervention`([…])	Greedily pick `num_interventions` single node interventions based on how many edges they orient.
`DAG.greedy_optimal_fully_orienting_interventions`([cpdag])	Find a set of interventions which fully orients a CPDAG into this DAG, using greedy selection of the interventions.