From 20661f1eadf64bf359a88afc09a8e5fed29123c5 Mon Sep 17 00:00:00 2001 From: Jaron Lee Date: Tue, 14 Mar 2023 09:34:54 -0400 Subject: [PATCH] Add ananke graph export functionality (#63) * add ananke files export * fix tests and extend to all graphs on directed, undirected, bidirected edges * use CPDAG as standin for CG --------- Signed-off-by: Jaron Lee Co-authored-by: Adam Li --- docs/whats_new/v0.1.rst | 1 + poetry.lock | 3514 +++++++++++----------- pyproject.toml | 2 +- pywhy_graphs/export/__init__.py | 1 + pywhy_graphs/export/ananke.py | 121 + pywhy_graphs/export/tests/test_ananke.py | 122 + 6 files changed, 2065 insertions(+), 1696 deletions(-) create mode 100644 pywhy_graphs/export/ananke.py create mode 100644 pywhy_graphs/export/tests/test_ananke.py diff --git a/docs/whats_new/v0.1.rst b/docs/whats_new/v0.1.rst index 356a21606..0ed38ce83 100644 --- a/docs/whats_new/v0.1.rst +++ b/docs/whats_new/v0.1.rst @@ -41,6 +41,7 @@ Changelog - |MajorFeature| Implement a series of graph classes for modeling interventions, such as :class:`pywhy_graphs.AugmentedGraph`, by `Adam Li`_ (:pr:`49`) - |Feature| Implement export/import functions to go to/from pywhy-graphs to causallearn and to/from pywhy-graphs to numpy, by `Adam Li`_ (:pr:`51`) - |Feature| Implement export/import functions to go to/from pywhy-graphs to pcalg and tetrad, by `Adam Li`_ (:pr:`60`) +- |Feature| Implement export/import functions to go to/from pywhy-graphs to ananke-causal, by `Jaron Lee`_ (:pr:`63`) Code and Documentation Contributors ----------------------------------- diff --git a/poetry.lock b/poetry.lock index d575c7c6c..92435e0e0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,38 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + [[package]] name = "alabaster" -version = "0.7.12" +version = "0.7.13" description = "A configurable sidebar-enabled Sphinx theme" category = "dev" optional = false -python-versions = "*" +python-versions = ">=3.6" 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+from .ananke import ananke_to_graph, graph_to_ananke from .causallearn import clearn_to_graph, graph_to_arr, graph_to_clearn from .numpy import graph_to_numpy, numpy_to_graph from .pcalg import graph_to_pcalg, pcalg_to_graph diff --git a/pywhy_graphs/export/ananke.py b/pywhy_graphs/export/ananke.py new file mode 100644 index 000000000..2385939c2 --- /dev/null +++ b/pywhy_graphs/export/ananke.py @@ -0,0 +1,121 @@ +import networkx as nx + +import pywhy_graphs +import pywhy_graphs.networkx as pywhy_nx + + +def graph_to_ananke( + graph: pywhy_nx.MixedEdgeGraph, + directed_edge_name="directed", + bidirected_edge_name="bidirected", + undirected_edge_name="undirected", +): + """ + Convert causal graph to Ananke graph. Supports graphs with directed, undirected, and + bidirected edges -- including DAGs, ADMGs, and CGs (chain graphs). + + Parameters + ---------- + graph : pywhy_nx.MixedEdgeGraph + The mixed edge causal graph + directed_edge_name : str + Name of the directed edge, default is 'directed'. + bidirected_edge_name : str + Name of the bidirected edge, default is 'bidirected'. + undirected_edge_name : str + Name of the undirected edge, default is 'undirected'. + Returns + ------- + result : Graph + The Ananke graph + + """ + from ananke.graphs import ADMG, BG, CG, DAG, SG, UG + + vertices = graph.nodes + has_directed = False + has_bidirected = False + has_undirected = False + for edge_type, sub_graph in graph.get_graphs().items(): + if sub_graph.edges: + if edge_type == directed_edge_name: + has_directed = True + di_edges = [e for e in sub_graph.edges] + elif edge_type == bidirected_edge_name: + has_bidirected = True + bi_edges = [e for e in sub_graph.edges] + elif edge_type == undirected_edge_name: + has_undirected = True + ud_edges = [e for e in sub_graph.edges] + if has_directed and not has_bidirected and not has_undirected: + result = DAG(vertices, di_edges) + elif has_directed and has_bidirected and not has_undirected: + result = ADMG(vertices, di_edges=di_edges, bi_edges=bi_edges) + elif has_directed and not has_bidirected and has_undirected: + result = CG(vertices, di_edges=di_edges, ud_edges=ud_edges) + elif not has_directed and has_bidirected and not has_undirected: + result = BG(vertices, bi_edges=bi_edges) + elif not has_directed and not has_bidirected and has_undirected: + result = UG(vertices, ud_edges=ud_edges) + else: + result = SG(vertices, di_edges=di_edges, bi_edges=bi_edges, ud_edges=ud_edges) + + return result + + +def ananke_to_graph( + ananke_graph, + directed_edge_name="directed", + bidirected_edge_name="bidirected", + undirected_edge_name="undirected", +) -> pywhy_nx.MixedEdgeGraph: + """ + Convert Ananke graph to causal graph. Explicitly supports conversion + to pywhy_nx.DAG, pywhy_nx.ADMG, and for ananke.graphs.CG to pywhy_nx.CPDAG. + Other graph types are converted to a pywhy_nx.MixedEdgeGraph. + + Parameters + ---------- + ananke_graph : Graph + The Ananke graph + directed_edge_name : str + Name of the directed edge, default is 'directed'. + bidirected_edge_name : str + Name of the bidirected edge, default is 'bidirected'. + undirected_edge_name : str + Name of the undirected edge, default is 'undirected'. + Returns + ------- + result : pywhy_nx.MixedEdgeGraph + The mixed edge graph. + """ + from ananke.graphs import ADMG, CG, DAG + + if type(ananke_graph) == DAG: + graph = pywhy_graphs.ADMG() + graph.add_nodes_from(ananke_graph.vertices) + graph.add_edges_from(ananke_graph.di_edges, edge_type=directed_edge_name) + elif type(ananke_graph) == ADMG: + graph = pywhy_graphs.ADMG() + graph.add_nodes_from(ananke_graph.vertices) + graph.add_edges_from(ananke_graph.di_edges, edge_type=directed_edge_name) + graph.add_edges_from(ananke_graph.bi_edges, edge_type=bidirected_edge_name) + elif type(ananke_graph) == CG: + graph = pywhy_graphs.CPDAG() + graph.add_nodes_from(ananke_graph.vertices) + graph.add_edges_from(ananke_graph.di_edges, edge_type=directed_edge_name) + graph.add_edges_from(ananke_graph.ud_edges, edge_type=undirected_edge_name) + else: + graph = pywhy_nx.MixedEdgeGraph() + graph.add_nodes_from(ananke_graph.vertices) + if ananke_graph.di_edges: + directed_edges = nx.DiGraph(ananke_graph.di_edges) + graph.add_edge_type(directed_edges, directed_edge_name) + if ananke_graph.bi_edges: + bidirected_edges = nx.Graph(ananke_graph.bi_edges) + graph.add_edge_type(bidirected_edges, bidirected_edge_name) + if ananke_graph.ud_edges: + undirected_edges = nx.Graph(ananke_graph.ud_edges) + graph.add_edge_type(undirected_edges, undirected_edge_name) + + return graph diff --git a/pywhy_graphs/export/tests/test_ananke.py b/pywhy_graphs/export/tests/test_ananke.py new file mode 100644 index 000000000..66dc84318 --- /dev/null +++ b/pywhy_graphs/export/tests/test_ananke.py @@ -0,0 +1,122 @@ +import networkx as nx +import pytest +from ananke.graphs import ADMG, BG, CG, DAG, SG, UG, Graph + +import pywhy_graphs +import pywhy_graphs.networkx as pywhy_nx +from pywhy_graphs.export import ananke_to_graph, graph_to_ananke +from pywhy_graphs.testing import assert_mixed_edge_graphs_isomorphic + + +def dag(): + + vertices = ["A", "B", "C", "D"] + di_edges = [("A", "B"), ("B", "C"), ("C", "D")] + graph = DAG(vertices=vertices, di_edges=di_edges) + expected_graph = pywhy_graphs.ADMG(di_edges, bi_edges=[]) + + return graph, expected_graph + + +def admg(): + + vertices = ["A", "B", "C", "D"] + di_edges = [("A", "B"), ("B", "C"), ("C", "D")] + bi_edges = [("A", "C"), ("B", "D")] + graph = ADMG(vertices=vertices, di_edges=di_edges, bi_edges=bi_edges) + + directed_edges = nx.DiGraph(di_edges) + bidirected_edges = nx.Graph(bi_edges) + expected_graph = pywhy_graphs.ADMG(directed_edges, bidirected_edges) + expected_graph.add_nodes_from(vertices) + + return graph, expected_graph + + +def cg(): + # No partially directed cycles allowed, currently using CPDAG to represent + # a chain graph + vertices = ["A", "B", "C", "D"] + di_edges = [("A", "C"), ("B", "D")] + ud_edges = [("B", "A"), ("C", "D")] + graph = CG(vertices=vertices, di_edges=di_edges, ud_edges=ud_edges) + + directed_edges = nx.DiGraph(di_edges) + undirected_edges = nx.Graph(ud_edges) + + expected_graph = pywhy_graphs.CPDAG(directed_edges, undirected_edges) + expected_graph.add_nodes_from(vertices) + + return graph, expected_graph + + +def ug(): + vertices = ["A", "B", "C", "D"] + ud_edges = [("A", "B"), ("B", "C"), ("C", "D"), ("D", "A")] + graph = UG(vertices, ud_edges=ud_edges) + + undirected_edges = nx.Graph(ud_edges) + expected_graph = pywhy_nx.MixedEdgeGraph() + expected_graph.add_nodes_from(vertices) + expected_graph.add_edge_type(undirected_edges, "undirected") + + return graph, expected_graph + + +def bg(): + vertices = ["A", "B", "C", "D"] + bi_edges = [("A", "B"), ("B", "C"), ("C", "D"), ("D", "A")] + graph = BG(vertices, bi_edges=bi_edges) + + bidirected_edges = nx.Graph(bi_edges) + expected_graph = pywhy_nx.MixedEdgeGraph() + expected_graph.add_nodes_from(vertices) + expected_graph.add_edge_type(bidirected_edges, "bidirected") + + return graph, expected_graph + + +def sg(): + # Segregated graph needs to satisfy the property that undirected and + # bidirected edges never meet, and that undirected edges can't be oriented + # to form a directed cycle + vertices = ["A", "B", "C", "D"] + di_edges = [("A", "C"), ("B", "D")] + bi_edges = [("A", "B")] + ud_edges = [("C", "D")] + graph = SG(vertices=vertices, di_edges=di_edges, ud_edges=ud_edges, bi_edges=bi_edges) + + directed_edges = nx.DiGraph(di_edges) + undirected_edges = nx.Graph(ud_edges) + bidirected_edges = nx.Graph(bi_edges) + + expected_graph = pywhy_nx.MixedEdgeGraph() + expected_graph.add_nodes_from(vertices) + expected_graph.add_edge_type(directed_edges, "directed") + expected_graph.add_edge_type(undirected_edges, "undirected") + expected_graph.add_edge_type(bidirected_edges, "bidirected") + + return graph, expected_graph + + +@pytest.mark.parametrize( + "ananke_G, expected_G, graph_type", + [ + [*dag(), "dag"], + [*admg(), "admg"], + [*cg(), "cg"], + [*ug(), "ug"], + [*bg(), "bg"], + [*sg(), "sg"], + ], +) +def test_graph_to_ananke_roundtrip( + ananke_G: Graph, expected_G: pywhy_nx.MixedEdgeGraph, graph_type: str +): + # Convert to Ananke graph + result = graph_to_ananke(expected_G) + + # Convert back to MixedEdgeGraph + graph = ananke_to_graph(result) + + assert assert_mixed_edge_graphs_isomorphic(graph, expected_G)