cliquepicking 0.2.2

Counting and Sampling Markov Equivalent DAGs

cliquepicking

You can install the cliquepicking module with pip install cliquepicking.

The module provides the functions

• mec_size(G), which outputs the number of DAGs in the MEC represented by CPDAG G
• mec_sample_dags(G, k), which returns k uniformly sampled DAGs from the MEC represented by CPDAG G
• mec_sample_orders(G, k) which returns topological orders of k uniformly sampled DAGs from the MEC represented by CPDAG G

Be aware that mec_sample_dags(G, k) holds (and returns) k DAGs in memory. (For large graphs) to avoid high memory demand, generate DAGs in smaller batches or use mec_sample_orders(G, k), which only returns the easier-to-store topological order.

In all cases, G should be given as an edge list (vertices should be represented by zero-indexed integers), which includes (a, b) and (b, a) for undirected edges $a - b$ and only (a, b) for directed edges $a \rightarrow b$. E.g.

import cliquepicking as cp

edges = [(0, 1), (1, 0), (1, 2), (2, 1), (0, 3), (2, 3)]
print(cp.mec_size(edges))

computes the MEC size for the graph with edges $0 - 1 - 2$ and $0 \rightarrow 3 \leftarrow 2$ and hence the code should print out 3.

For a more involved example (Example 3 from the AAAI paper), which illustrates use with networkx, see:

import cliquepicking as cp
import networkx as nx

G = nx.DiGraph()
G.add_edge(0, 1)
G.add_edge(1, 0)
G.add_edge(0, 2)
G.add_edge(2, 0)
G.add_edge(1, 2)
G.add_edge(2, 1)
G.add_edge(1, 3)
G.add_edge(3, 1)
G.add_edge(1, 4)
G.add_edge(4, 1)
G.add_edge(1, 5)
G.add_edge(5, 1)
G.add_edge(2, 3)
G.add_edge(3, 2)
G.add_edge(2, 4)
G.add_edge(4, 2)
G.add_edge(2, 5)
G.add_edge(5, 2)
G.add_edge(3, 4)
G.add_edge(4, 3)
G.add_edge(4, 5)
G.add_edge(5, 4)
print(cp.mec_size(list(G.edges)))
sample_dags = cp.mec_sample_dags(list(G.edges), 5)
for dag in sample_dags:
    print(nx.DiGraph(dag))
sample_orders = cp.mec_sample_orders(list(G.edges), 5)
for order in sample_orders:
    print(order)

Time Complexity

The counting procedure has worst-case run-time $n^3$, with $n$ denoting the number of vertices of the input graph. This improves the asymptotic complexity from the literature based on the comment at the bottom of page 80 in my thesis. That is, each recursive call is associated with a subtree of the clique-tree of $G$ shaving off a factor of $n$. By using global memoization the computations of function $\phi$ are also possible in this run-time. The general procedure is identical to the published algorithm. There are a few additional practical optimizations that do not further improve the worst-case run-time but make the algorithm faster in practice compared to previous implementations.