# 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.
```python
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:
```python
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](https://mwien.github.io/thesis.pdf). 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.
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"description": "# cliquepicking\n\nYou can install the cliquepicking module with ```pip install cliquepicking```. \n\nThe module provides the functions\n\n- ```mec_size(G)```, which outputs the number of DAGs in the MEC represented by CPDAG G\n- ```mec_sample_dags(G, k)```, which returns k uniformly sampled DAGs from the MEC represented by CPDAG G\n- ```mec_sample_orders(G, k)``` which returns topological orders of k uniformly sampled DAGs from the MEC represented by CPDAG G\n\nBe 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. \n\nIn 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.\n\n```python\nimport cliquepicking as cp\n\nedges = [(0, 1), (1, 0), (1, 2), (2, 1), (0, 3), (2, 3)]\nprint(cp.mec_size(edges))\n```\n\ncomputes 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```.\n\nFor a more involved example (Example 3 from the AAAI paper), which illustrates use with networkx, see:\n\n```python\nimport cliquepicking as cp\nimport networkx as nx\n\nG = nx.DiGraph()\nG.add_edge(0, 1)\nG.add_edge(1, 0)\nG.add_edge(0, 2)\nG.add_edge(2, 0)\nG.add_edge(1, 2)\nG.add_edge(2, 1)\nG.add_edge(1, 3)\nG.add_edge(3, 1)\nG.add_edge(1, 4)\nG.add_edge(4, 1)\nG.add_edge(1, 5)\nG.add_edge(5, 1)\nG.add_edge(2, 3)\nG.add_edge(3, 2)\nG.add_edge(2, 4)\nG.add_edge(4, 2)\nG.add_edge(2, 5)\nG.add_edge(5, 2)\nG.add_edge(3, 4)\nG.add_edge(4, 3)\nG.add_edge(4, 5)\nG.add_edge(5, 4)\nprint(cp.mec_size(list(G.edges)))\nsample_dags = cp.mec_sample_dags(list(G.edges), 5)\nfor dag in sample_dags:\n print(nx.DiGraph(dag))\nsample_orders = cp.mec_sample_orders(list(G.edges), 5)\nfor order in sample_orders:\n print(order)\n```\n\n## Time Complexity\nThe 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](https://mwien.github.io/thesis.pdf). 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. \n\n",
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