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tuning-competition-baseline/.venv/lib/python3.11/site-packages/distutils-precedence.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2638ce9e2500e572a5e0de7faed6661eb569d1b696fcba07b0dd223da5f5d224
+size 151

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/bipartite/tests/test_basic.py

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+import pytest
+
+import networkx as nx
+from networkx.algorithms import bipartite
+
+
+class TestBipartiteBasic:
+    def test_is_bipartite(self):
+        assert bipartite.is_bipartite(nx.path_graph(4))
+        assert bipartite.is_bipartite(nx.DiGraph([(1, 0)]))
+        assert not bipartite.is_bipartite(nx.complete_graph(3))
+
+    def test_bipartite_color(self):
+        G = nx.path_graph(4)
+        c = bipartite.color(G)
+        assert c == {0: 1, 1: 0, 2: 1, 3: 0}
+
+    def test_not_bipartite_color(self):
+        with pytest.raises(nx.NetworkXError):
+            c = bipartite.color(nx.complete_graph(4))
+
+    def test_bipartite_directed(self):
+        G = bipartite.random_graph(10, 10, 0.1, directed=True)
+        assert bipartite.is_bipartite(G)
+
+    def test_bipartite_sets(self):
+        G = nx.path_graph(4)
+        X, Y = bipartite.sets(G)
+        assert X == {0, 2}
+        assert Y == {1, 3}
+
+    def test_bipartite_sets_directed(self):
+        G = nx.path_graph(4)
+        D = G.to_directed()
+        X, Y = bipartite.sets(D)
+        assert X == {0, 2}
+        assert Y == {1, 3}
+
+    def test_bipartite_sets_given_top_nodes(self):
+        G = nx.path_graph(4)
+        top_nodes = [0, 2]
+        X, Y = bipartite.sets(G, top_nodes)
+        assert X == {0, 2}
+        assert Y == {1, 3}
+
+    def test_bipartite_sets_disconnected(self):
+        with pytest.raises(nx.AmbiguousSolution):
+            G = nx.path_graph(4)
+            G.add_edges_from([(5, 6), (6, 7)])
+            X, Y = bipartite.sets(G)
+
+    def test_is_bipartite_node_set(self):
+        G = nx.path_graph(4)
+
+        with pytest.raises(nx.AmbiguousSolution):
+            bipartite.is_bipartite_node_set(G, [1, 1, 2, 3])
+
+        assert bipartite.is_bipartite_node_set(G, [0, 2])
+        assert bipartite.is_bipartite_node_set(G, [1, 3])
+        assert not bipartite.is_bipartite_node_set(G, [1, 2])
+        G.add_edge(10, 20)
+        assert bipartite.is_bipartite_node_set(G, [0, 2, 10])
+        assert bipartite.is_bipartite_node_set(G, [0, 2, 20])
+        assert bipartite.is_bipartite_node_set(G, [1, 3, 10])
+        assert bipartite.is_bipartite_node_set(G, [1, 3, 20])
+
+    def test_bipartite_density(self):
+        G = nx.path_graph(5)
+        X, Y = bipartite.sets(G)
+        density = len(list(G.edges())) / (len(X) * len(Y))
+        assert bipartite.density(G, X) == density
+        D = nx.DiGraph(G.edges())
+        assert bipartite.density(D, X) == density / 2.0
+        assert bipartite.density(nx.Graph(), {}) == 0.0
+
+    def test_bipartite_degrees(self):
+        G = nx.path_graph(5)
+        X = {1, 3}
+        Y = {0, 2, 4}
+        u, d = bipartite.degrees(G, Y)
+        assert dict(u) == {1: 2, 3: 2}
+        assert dict(d) == {0: 1, 2: 2, 4: 1}
+
+    def test_bipartite_weighted_degrees(self):
+        G = nx.path_graph(5)
+        G.add_edge(0, 1, weight=0.1, other=0.2)
+        X = {1, 3}
+        Y = {0, 2, 4}
+        u, d = bipartite.degrees(G, Y, weight="weight")
+        assert dict(u) == {1: 1.1, 3: 2}
+        assert dict(d) == {0: 0.1, 2: 2, 4: 1}
+        u, d = bipartite.degrees(G, Y, weight="other")
+        assert dict(u) == {1: 1.2, 3: 2}
+        assert dict(d) == {0: 0.2, 2: 2, 4: 1}
+
+    def test_biadjacency_matrix_weight(self):
+        pytest.importorskip("scipy")
+        G = nx.path_graph(5)
+        G.add_edge(0, 1, weight=2, other=4)
+        X = [1, 3]
+        Y = [0, 2, 4]
+        M = bipartite.biadjacency_matrix(G, X, weight="weight")
+        assert M[0, 0] == 2
+        M = bipartite.biadjacency_matrix(G, X, weight="other")
+        assert M[0, 0] == 4
+
+    def test_biadjacency_matrix(self):
+        pytest.importorskip("scipy")
+        tops = [2, 5, 10]
+        bots = [5, 10, 15]
+        for i in range(len(tops)):
+            G = bipartite.random_graph(tops[i], bots[i], 0.2)
+            top = [n for n, d in G.nodes(data=True) if d["bipartite"] == 0]
+            M = bipartite.biadjacency_matrix(G, top)
+            assert M.shape[0] == tops[i]
+            assert M.shape[1] == bots[i]
+
+    def test_biadjacency_matrix_order(self):
+        pytest.importorskip("scipy")
+        G = nx.path_graph(5)
+        G.add_edge(0, 1, weight=2)
+        X = [3, 1]
+        Y = [4, 2, 0]
+        M = bipartite.biadjacency_matrix(G, X, Y, weight="weight")
+        assert M[1, 2] == 2

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/bipartite/tests/test_cluster.py

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+import pytest
+
+import networkx as nx
+from networkx.algorithms import bipartite
+from networkx.algorithms.bipartite.cluster import cc_dot, cc_max, cc_min
+
+
+def test_pairwise_bipartite_cc_functions():
+    # Test functions for different kinds of bipartite clustering coefficients
+    # between pairs of nodes using 3 example graphs from figure 5 p. 40
+    # Latapy et al (2008)
+    G1 = nx.Graph([(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7)])
+    G2 = nx.Graph([(0, 2), (0, 3), (0, 4), (1, 3), (1, 4), (1, 5)])
+    G3 = nx.Graph(
+        [(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9)]
+    )
+    result = {
+        0: [1 / 3.0, 2 / 3.0, 2 / 5.0],
+        1: [1 / 2.0, 2 / 3.0, 2 / 3.0],
+        2: [2 / 8.0, 2 / 5.0, 2 / 5.0],
+    }
+    for i, G in enumerate([G1, G2, G3]):
+        assert bipartite.is_bipartite(G)
+        assert cc_dot(set(G[0]), set(G[1])) == result[i][0]
+        assert cc_min(set(G[0]), set(G[1])) == result[i][1]
+        assert cc_max(set(G[0]), set(G[1])) == result[i][2]
+
+
+def test_star_graph():
+    G = nx.star_graph(3)
+    # all modes are the same
+    answer = {0: 0, 1: 1, 2: 1, 3: 1}
+    assert bipartite.clustering(G, mode="dot") == answer
+    assert bipartite.clustering(G, mode="min") == answer
+    assert bipartite.clustering(G, mode="max") == answer
+
+
+def test_not_bipartite():
+    with pytest.raises(nx.NetworkXError):
+        bipartite.clustering(nx.complete_graph(4))
+
+
+def test_bad_mode():
+    with pytest.raises(nx.NetworkXError):
+        bipartite.clustering(nx.path_graph(4), mode="foo")
+
+
+def test_path_graph():
+    G = nx.path_graph(4)
+    answer = {0: 0.5, 1: 0.5, 2: 0.5, 3: 0.5}
+    assert bipartite.clustering(G, mode="dot") == answer
+    assert bipartite.clustering(G, mode="max") == answer
+    answer = {0: 1, 1: 1, 2: 1, 3: 1}
+    assert bipartite.clustering(G, mode="min") == answer
+
+
+def test_average_path_graph():
+    G = nx.path_graph(4)
+    assert bipartite.average_clustering(G, mode="dot") == 0.5
+    assert bipartite.average_clustering(G, mode="max") == 0.5
+    assert bipartite.average_clustering(G, mode="min") == 1
+
+
+def test_ra_clustering_davis():
+    G = nx.davis_southern_women_graph()
+    cc4 = round(bipartite.robins_alexander_clustering(G), 3)
+    assert cc4 == 0.468
+
+
+def test_ra_clustering_square():
+    G = nx.path_graph(4)
+    G.add_edge(0, 3)
+    assert bipartite.robins_alexander_clustering(G) == 1.0
+
+
+def test_ra_clustering_zero():
+    G = nx.Graph()
+    assert bipartite.robins_alexander_clustering(G) == 0
+    G.add_nodes_from(range(4))
+    assert bipartite.robins_alexander_clustering(G) == 0
+    G.add_edges_from([(0, 1), (2, 3), (3, 4)])
+    assert bipartite.robins_alexander_clustering(G) == 0
+    G.add_edge(1, 2)
+    assert bipartite.robins_alexander_clustering(G) == 0

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/bipartite/tests/test_covering.py

@@ -0,0 +1,33 @@
+import networkx as nx
+from networkx.algorithms import bipartite
+
+
+class TestMinEdgeCover:
+    """Tests for :func:`networkx.algorithms.bipartite.min_edge_cover`"""
+
+    def test_empty_graph(self):
+        G = nx.Graph()
+        assert bipartite.min_edge_cover(G) == set()
+
+    def test_graph_single_edge(self):
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        assert bipartite.min_edge_cover(G) == {(0, 1), (1, 0)}
+
+    def test_bipartite_default(self):
+        G = nx.Graph()
+        G.add_nodes_from([1, 2, 3, 4], bipartite=0)
+        G.add_nodes_from(["a", "b", "c"], bipartite=1)
+        G.add_edges_from([(1, "a"), (1, "b"), (2, "b"), (2, "c"), (3, "c"), (4, "a")])
+        min_cover = bipartite.min_edge_cover(G)
+        assert nx.is_edge_cover(G, min_cover)
+        assert len(min_cover) == 8
+
+    def test_bipartite_explicit(self):
+        G = nx.Graph()
+        G.add_nodes_from([1, 2, 3, 4], bipartite=0)
+        G.add_nodes_from(["a", "b", "c"], bipartite=1)
+        G.add_edges_from([(1, "a"), (1, "b"), (2, "b"), (2, "c"), (3, "c"), (4, "a")])
+        min_cover = bipartite.min_edge_cover(G, bipartite.eppstein_matching)
+        assert nx.is_edge_cover(G, min_cover)
+        assert len(min_cover) == 8

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/bipartite/tests/test_extendability.py

@@ -0,0 +1,326 @@
+import pytest
+
+import networkx as nx
+
+
+def test_selfloops_raises():
+    G = nx.ladder_graph(3)
+    G.add_edge(0, 0)
+    with pytest.raises(nx.NetworkXError, match=".*not bipartite"):
+        nx.bipartite.maximal_extendability(G)
+
+
+def test_disconnected_raises():
+    G = nx.ladder_graph(3)
+    G.add_node("a")
+    with pytest.raises(nx.NetworkXError, match=".*not connected"):
+        nx.bipartite.maximal_extendability(G)
+
+
+def test_not_bipartite_raises():
+    G = nx.complete_graph(5)
+    with pytest.raises(nx.NetworkXError, match=".*not bipartite"):
+        nx.bipartite.maximal_extendability(G)
+
+
+def test_no_perfect_matching_raises():
+    G = nx.Graph([(0, 1), (0, 2)])
+    with pytest.raises(nx.NetworkXError, match=".*not contain a perfect matching"):
+        nx.bipartite.maximal_extendability(G)
+
+
+def test_ladder_graph_is_1():
+    G = nx.ladder_graph(3)
+    assert nx.bipartite.maximal_extendability(G) == 1
+
+
+def test_cubical_graph_is_2():
+    G = nx.cubical_graph()
+    assert nx.bipartite.maximal_extendability(G) == 2
+
+
+def test_k_is_3():
+    G = nx.Graph(
+        [
+            (1, 6),
+            (1, 7),
+            (1, 8),
+            (1, 9),
+            (2, 6),
+            (2, 7),
+            (2, 8),
+            (2, 10),
+            (3, 6),
+            (3, 8),
+            (3, 9),
+            (3, 10),
+            (4, 7),
+            (4, 8),
+            (4, 9),
+            (4, 10),
+            (5, 6),
+            (5, 7),
+            (5, 9),
+            (5, 10),
+        ]
+    )
+    assert nx.bipartite.maximal_extendability(G) == 3
+
+
+def test_k_is_4():
+    G = nx.Graph(
+        [
+            (8, 1),
+            (8, 2),
+            (8, 3),
+            (8, 4),
+            (8, 5),
+            (9, 1),
+            (9, 2),
+            (9, 3),
+            (9, 4),
+            (9, 7),
+            (10, 1),
+            (10, 2),
+            (10, 3),
+            (10, 4),
+            (10, 6),
+            (11, 1),
+            (11, 2),
+            (11, 5),
+            (11, 6),
+            (11, 7),
+            (12, 1),
+            (12, 3),
+            (12, 5),
+            (12, 6),
+            (12, 7),
+            (13, 2),
+            (13, 4),
+            (13, 5),
+            (13, 6),
+            (13, 7),
+            (14, 3),
+            (14, 4),
+            (14, 5),
+            (14, 6),
+            (14, 7),
+        ]
+    )
+    assert nx.bipartite.maximal_extendability(G) == 4
+
+
+def test_k_is_5():
+    G = nx.Graph(
+        [
+            (8, 1),
+            (8, 2),
+            (8, 3),
+            (8, 4),
+            (8, 5),
+            (8, 6),
+            (9, 1),
+            (9, 2),
+            (9, 3),
+            (9, 4),
+            (9, 5),
+            (9, 7),
+            (10, 1),
+            (10, 2),
+            (10, 3),
+            (10, 4),
+            (10, 6),
+            (10, 7),
+            (11, 1),
+            (11, 2),
+            (11, 3),
+            (11, 5),
+            (11, 6),
+            (11, 7),
+            (12, 1),
+            (12, 2),
+            (12, 4),
+            (12, 5),
+            (12, 6),
+            (12, 7),
+            (13, 1),
+            (13, 3),
+            (13, 4),
+            (13, 5),
+            (13, 6),
+            (13, 7),
+            (14, 2),
+            (14, 3),
+            (14, 4),
+            (14, 5),
+            (14, 6),
+            (14, 7),
+        ]
+    )
+    assert nx.bipartite.maximal_extendability(G) == 5
+
+
+def test_k_is_6():
+    G = nx.Graph(
+        [
+            (9, 1),
+            (9, 2),
+            (9, 3),
+            (9, 4),
+            (9, 5),
+            (9, 6),
+            (9, 7),
+            (10, 1),
+            (10, 2),
+            (10, 3),
+            (10, 4),
+            (10, 5),
+            (10, 6),
+            (10, 8),
+            (11, 1),
+            (11, 2),
+            (11, 3),
+            (11, 4),
+            (11, 5),
+            (11, 7),
+            (11, 8),
+            (12, 1),
+            (12, 2),
+            (12, 3),
+            (12, 4),
+            (12, 6),
+            (12, 7),
+            (12, 8),
+            (13, 1),
+            (13, 2),
+            (13, 3),
+            (13, 5),
+            (13, 6),
+            (13, 7),
+            (13, 8),
+            (14, 1),
+            (14, 2),
+            (14, 4),
+            (14, 5),
+            (14, 6),
+            (14, 7),
+            (14, 8),
+            (15, 1),
+            (15, 3),
+            (15, 4),
+            (15, 5),
+            (15, 6),
+            (15, 7),
+            (15, 8),
+            (16, 2),
+            (16, 3),
+            (16, 4),
+            (16, 5),
+            (16, 6),
+            (16, 7),
+            (16, 8),
+        ]
+    )
+    assert nx.bipartite.maximal_extendability(G) == 6
+
+
+def test_k_is_7():
+    G = nx.Graph(
+        [
+            (1, 11),
+            (1, 12),
+            (1, 13),
+            (1, 14),
+            (1, 15),
+            (1, 16),
+            (1, 17),
+            (1, 18),
+            (2, 11),
+            (2, 12),
+            (2, 13),
+            (2, 14),
+            (2, 15),
+            (2, 16),
+            (2, 17),
+            (2, 19),
+            (3, 11),
+            (3, 12),
+            (3, 13),
+            (3, 14),
+            (3, 15),
+            (3, 16),
+            (3, 17),
+            (3, 20),
+            (4, 11),
+            (4, 12),
+            (4, 13),
+            (4, 14),
+            (4, 15),
+            (4, 16),
+            (4, 17),
+            (4, 18),
+            (4, 19),
+            (4, 20),
+            (5, 11),
+            (5, 12),
+            (5, 13),
+            (5, 14),
+            (5, 15),
+            (5, 16),
+            (5, 17),
+            (5, 18),
+            (5, 19),
+            (5, 20),
+            (6, 11),
+            (6, 12),
+            (6, 13),
+            (6, 14),
+            (6, 15),
+            (6, 16),
+            (6, 17),
+            (6, 18),
+            (6, 19),
+            (6, 20),
+            (7, 11),
+            (7, 12),
+            (7, 13),
+            (7, 14),
+            (7, 15),
+            (7, 16),
+            (7, 17),
+            (7, 18),
+            (7, 19),
+            (7, 20),
+            (8, 11),
+            (8, 12),
+            (8, 13),
+            (8, 14),
+            (8, 15),
+            (8, 16),
+            (8, 17),
+            (8, 18),
+            (8, 19),
+            (8, 20),
+            (9, 11),
+            (9, 12),
+            (9, 13),
+            (9, 14),
+            (9, 15),
+            (9, 16),
+            (9, 17),
+            (9, 18),
+            (9, 19),
+            (9, 20),
+            (10, 11),
+            (10, 12),
+            (10, 13),
+            (10, 14),
+            (10, 15),
+            (10, 16),
+            (10, 17),
+            (10, 18),
+            (10, 19),
+            (10, 20),
+        ]
+    )
+    assert nx.bipartite.maximal_extendability(G) == 7

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/bipartite/tests/test_generators.py

@@ -0,0 +1,400 @@
+import numbers
+
+import pytest
+
+import networkx as nx
+
+from ..generators import (
+    alternating_havel_hakimi_graph,
+    complete_bipartite_graph,
+    configuration_model,
+    gnmk_random_graph,
+    havel_hakimi_graph,
+    preferential_attachment_graph,
+    random_graph,
+    reverse_havel_hakimi_graph,
+)
+
+"""
+Generators - Bipartite
+----------------------
+"""
+
+
+class TestGeneratorsBipartite:
+    def test_complete_bipartite_graph(self):
+        G = complete_bipartite_graph(0, 0)
+        assert nx.is_isomorphic(G, nx.null_graph())
+
+        for i in [1, 5]:
+            G = complete_bipartite_graph(i, 0)
+            assert nx.is_isomorphic(G, nx.empty_graph(i))
+            G = complete_bipartite_graph(0, i)
+            assert nx.is_isomorphic(G, nx.empty_graph(i))
+
+        G = complete_bipartite_graph(2, 2)
+        assert nx.is_isomorphic(G, nx.cycle_graph(4))
+
+        G = complete_bipartite_graph(1, 5)
+        assert nx.is_isomorphic(G, nx.star_graph(5))
+
+        G = complete_bipartite_graph(5, 1)
+        assert nx.is_isomorphic(G, nx.star_graph(5))
+
+        # complete_bipartite_graph(m1,m2) is a connected graph with
+        # m1+m2 nodes and  m1*m2 edges
+        for m1, m2 in [(5, 11), (7, 3)]:
+            G = complete_bipartite_graph(m1, m2)
+            assert nx.number_of_nodes(G) == m1 + m2
+            assert nx.number_of_edges(G) == m1 * m2
+
+        with pytest.raises(nx.NetworkXError):
+            complete_bipartite_graph(7, 3, create_using=nx.DiGraph)
+        with pytest.raises(nx.NetworkXError):
+            complete_bipartite_graph(7, 3, create_using=nx.MultiDiGraph)
+
+        mG = complete_bipartite_graph(7, 3, create_using=nx.MultiGraph)
+        assert mG.is_multigraph()
+        assert sorted(mG.edges()) == sorted(G.edges())
+
+        mG = complete_bipartite_graph(7, 3, create_using=nx.MultiGraph)
+        assert mG.is_multigraph()
+        assert sorted(mG.edges()) == sorted(G.edges())
+
+        mG = complete_bipartite_graph(7, 3)  # default to Graph
+        assert sorted(mG.edges()) == sorted(G.edges())
+        assert not mG.is_multigraph()
+        assert not mG.is_directed()
+
+        # specify nodes rather than number of nodes
+        for n1, n2 in [([1, 2], "ab"), (3, 2), (3, "ab"), ("ab", 3)]:
+            G = complete_bipartite_graph(n1, n2)
+            if isinstance(n1, numbers.Integral):
+                if isinstance(n2, numbers.Integral):
+                    n2 = range(n1, n1 + n2)
+                n1 = range(n1)
+            elif isinstance(n2, numbers.Integral):
+                n2 = range(n2)
+            edges = {(u, v) for u in n1 for v in n2}
+            assert edges == set(G.edges)
+            assert G.size() == len(edges)
+
+        # raise when node sets are not distinct
+        for n1, n2 in [([1, 2], 3), (3, [1, 2]), ("abc", "bcd")]:
+            pytest.raises(nx.NetworkXError, complete_bipartite_graph, n1, n2)
+
+    def test_configuration_model(self):
+        aseq = []
+        bseq = []
+        G = configuration_model(aseq, bseq)
+        assert len(G) == 0
+
+        aseq = [0, 0]
+        bseq = [0, 0]
+        G = configuration_model(aseq, bseq)
+        assert len(G) == 4
+        assert G.number_of_edges() == 0
+
+        aseq = [3, 3, 3, 3]
+        bseq = [2, 2, 2, 2, 2]
+        pytest.raises(nx.NetworkXError, configuration_model, aseq, bseq)
+
+        aseq = [3, 3, 3, 3]
+        bseq = [2, 2, 2, 2, 2, 2]
+        G = configuration_model(aseq, bseq)
+        assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]
+
+        aseq = [2, 2, 2, 2, 2, 2]
+        bseq = [3, 3, 3, 3]
+        G = configuration_model(aseq, bseq)
+        assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]
+
+        aseq = [2, 2, 2, 1, 1, 1]
+        bseq = [3, 3, 3]
+        G = configuration_model(aseq, bseq)
+        assert G.is_multigraph()
+        assert not G.is_directed()
+        assert sorted(d for n, d in G.degree()) == [1, 1, 1, 2, 2, 2, 3, 3, 3]
+
+        GU = nx.projected_graph(nx.Graph(G), range(len(aseq)))
+        assert GU.number_of_nodes() == 6
+
+        GD = nx.projected_graph(nx.Graph(G), range(len(aseq), len(aseq) + len(bseq)))
+        assert GD.number_of_nodes() == 3
+
+        G = reverse_havel_hakimi_graph(aseq, bseq, create_using=nx.Graph)
+        assert not G.is_multigraph()
+        assert not G.is_directed()
+
+        pytest.raises(
+            nx.NetworkXError, configuration_model, aseq, bseq, create_using=nx.DiGraph()
+        )
+        pytest.raises(
+            nx.NetworkXError, configuration_model, aseq, bseq, create_using=nx.DiGraph
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            configuration_model,
+            aseq,
+            bseq,
+            create_using=nx.MultiDiGraph,
+        )
+
+    def test_havel_hakimi_graph(self):
+        aseq = []
+        bseq = []
+        G = havel_hakimi_graph(aseq, bseq)
+        assert len(G) == 0
+
+        aseq = [0, 0]
+        bseq = [0, 0]
+        G = havel_hakimi_graph(aseq, bseq)
+        assert len(G) == 4
+        assert G.number_of_edges() == 0
+
+        aseq = [3, 3, 3, 3]
+        bseq = [2, 2, 2, 2, 2]
+        pytest.raises(nx.NetworkXError, havel_hakimi_graph, aseq, bseq)
+
+        bseq = [2, 2, 2, 2, 2, 2]
+        G = havel_hakimi_graph(aseq, bseq)
+        assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]
+
+        aseq = [2, 2, 2, 2, 2, 2]
+        bseq = [3, 3, 3, 3]
+        G = havel_hakimi_graph(aseq, bseq)
+        assert G.is_multigraph()
+        assert not G.is_directed()
+        assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]
+
+        GU = nx.projected_graph(nx.Graph(G), range(len(aseq)))
+        assert GU.number_of_nodes() == 6
+
+        GD = nx.projected_graph(nx.Graph(G), range(len(aseq), len(aseq) + len(bseq)))
+        assert GD.number_of_nodes() == 4
+
+        G = reverse_havel_hakimi_graph(aseq, bseq, create_using=nx.Graph)
+        assert not G.is_multigraph()
+        assert not G.is_directed()
+
+        pytest.raises(
+            nx.NetworkXError, havel_hakimi_graph, aseq, bseq, create_using=nx.DiGraph
+        )
+        pytest.raises(
+            nx.NetworkXError, havel_hakimi_graph, aseq, bseq, create_using=nx.DiGraph
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            havel_hakimi_graph,
+            aseq,
+            bseq,
+            create_using=nx.MultiDiGraph,
+        )
+
+    def test_reverse_havel_hakimi_graph(self):
+        aseq = []
+        bseq = []
+        G = reverse_havel_hakimi_graph(aseq, bseq)
+        assert len(G) == 0
+
+        aseq = [0, 0]
+        bseq = [0, 0]
+        G = reverse_havel_hakimi_graph(aseq, bseq)
+        assert len(G) == 4
+        assert G.number_of_edges() == 0
+
+        aseq = [3, 3, 3, 3]
+        bseq = [2, 2, 2, 2, 2]
+        pytest.raises(nx.NetworkXError, reverse_havel_hakimi_graph, aseq, bseq)
+
+        bseq = [2, 2, 2, 2, 2, 2]
+        G = reverse_havel_hakimi_graph(aseq, bseq)
+        assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]
+
+        aseq = [2, 2, 2, 2, 2, 2]
+        bseq = [3, 3, 3, 3]
+        G = reverse_havel_hakimi_graph(aseq, bseq)
+        assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]
+
+        aseq = [2, 2, 2, 1, 1, 1]
+        bseq = [3, 3, 3]
+        G = reverse_havel_hakimi_graph(aseq, bseq)
+        assert G.is_multigraph()
+        assert not G.is_directed()
+        assert sorted(d for n, d in G.degree()) == [1, 1, 1, 2, 2, 2, 3, 3, 3]
+
+        GU = nx.projected_graph(nx.Graph(G), range(len(aseq)))
+        assert GU.number_of_nodes() == 6
+
+        GD = nx.projected_graph(nx.Graph(G), range(len(aseq), len(aseq) + len(bseq)))
+        assert GD.number_of_nodes() == 3
+
+        G = reverse_havel_hakimi_graph(aseq, bseq, create_using=nx.Graph)
+        assert not G.is_multigraph()
+        assert not G.is_directed()
+
+        pytest.raises(
+            nx.NetworkXError,
+            reverse_havel_hakimi_graph,
+            aseq,
+            bseq,
+            create_using=nx.DiGraph,
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            reverse_havel_hakimi_graph,
+            aseq,
+            bseq,
+            create_using=nx.DiGraph,
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            reverse_havel_hakimi_graph,
+            aseq,
+            bseq,
+            create_using=nx.MultiDiGraph,
+        )
+
+    def test_alternating_havel_hakimi_graph(self):
+        aseq = []
+        bseq = []
+        G = alternating_havel_hakimi_graph(aseq, bseq)
+        assert len(G) == 0
+
+        aseq = [0, 0]
+        bseq = [0, 0]
+        G = alternating_havel_hakimi_graph(aseq, bseq)
+        assert len(G) == 4
+        assert G.number_of_edges() == 0
+
+        aseq = [3, 3, 3, 3]
+        bseq = [2, 2, 2, 2, 2]
+        pytest.raises(nx.NetworkXError, alternating_havel_hakimi_graph, aseq, bseq)
+
+        bseq = [2, 2, 2, 2, 2, 2]
+        G = alternating_havel_hakimi_graph(aseq, bseq)
+        assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]
+
+        aseq = [2, 2, 2, 2, 2, 2]
+        bseq = [3, 3, 3, 3]
+        G = alternating_havel_hakimi_graph(aseq, bseq)
+        assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]
+
+        aseq = [2, 2, 2, 1, 1, 1]
+        bseq = [3, 3, 3]
+        G = alternating_havel_hakimi_graph(aseq, bseq)
+        assert G.is_multigraph()
+        assert not G.is_directed()
+        assert sorted(d for n, d in G.degree()) == [1, 1, 1, 2, 2, 2, 3, 3, 3]
+
+        GU = nx.projected_graph(nx.Graph(G), range(len(aseq)))
+        assert GU.number_of_nodes() == 6
+
+        GD = nx.projected_graph(nx.Graph(G), range(len(aseq), len(aseq) + len(bseq)))
+        assert GD.number_of_nodes() == 3
+
+        G = reverse_havel_hakimi_graph(aseq, bseq, create_using=nx.Graph)
+        assert not G.is_multigraph()
+        assert not G.is_directed()
+
+        pytest.raises(
+            nx.NetworkXError,
+            alternating_havel_hakimi_graph,
+            aseq,
+            bseq,
+            create_using=nx.DiGraph,
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            alternating_havel_hakimi_graph,
+            aseq,
+            bseq,
+            create_using=nx.DiGraph,
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            alternating_havel_hakimi_graph,
+            aseq,
+            bseq,
+            create_using=nx.MultiDiGraph,
+        )
+
+    def test_preferential_attachment(self):
+        aseq = [3, 2, 1, 1]
+        G = preferential_attachment_graph(aseq, 0.5)
+        assert G.is_multigraph()
+        assert not G.is_directed()
+
+        G = preferential_attachment_graph(aseq, 0.5, create_using=nx.Graph)
+        assert not G.is_multigraph()
+        assert not G.is_directed()
+
+        pytest.raises(
+            nx.NetworkXError,
+            preferential_attachment_graph,
+            aseq,
+            0.5,
+            create_using=nx.DiGraph(),
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            preferential_attachment_graph,
+            aseq,
+            0.5,
+            create_using=nx.DiGraph(),
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            preferential_attachment_graph,
+            aseq,
+            0.5,
+            create_using=nx.DiGraph(),
+        )
+
+    def test_random_graph(self):
+        n = 10
+        m = 20
+        G = random_graph(n, m, 0.9)
+        assert len(G) == 30
+        assert nx.is_bipartite(G)
+        X, Y = nx.algorithms.bipartite.sets(G)
+        assert set(range(n)) == X
+        assert set(range(n, n + m)) == Y
+
+    def test_random_digraph(self):
+        n = 10
+        m = 20
+        G = random_graph(n, m, 0.9, directed=True)
+        assert len(G) == 30
+        assert nx.is_bipartite(G)
+        X, Y = nx.algorithms.bipartite.sets(G)
+        assert set(range(n)) == X
+        assert set(range(n, n + m)) == Y
+
+    def test_gnmk_random_graph(self):
+        n = 10
+        m = 20
+        edges = 100
+        # set seed because sometimes it is not connected
+        # which raises an error in bipartite.sets(G) below.
+        G = gnmk_random_graph(n, m, edges, seed=1234)
+        assert len(G) == n + m
+        assert nx.is_bipartite(G)
+        X, Y = nx.algorithms.bipartite.sets(G)
+        # print(X)
+        assert set(range(n)) == X
+        assert set(range(n, n + m)) == Y
+        assert edges == len(list(G.edges()))
+
+    def test_gnmk_random_graph_complete(self):
+        n = 10
+        m = 20
+        edges = 200
+        G = gnmk_random_graph(n, m, edges)
+        assert len(G) == n + m
+        assert nx.is_bipartite(G)
+        X, Y = nx.algorithms.bipartite.sets(G)
+        # print(X)
+        assert set(range(n)) == X
+        assert set(range(n, n + m)) == Y
+        assert edges == len(list(G.edges()))

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/bipartite/tests/test_matching.py

@@ -0,0 +1,326 @@
+"""Unit tests for the :mod:`networkx.algorithms.bipartite.matching` module."""
+import itertools
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms.bipartite.matching import (
+    eppstein_matching,
+    hopcroft_karp_matching,
+    maximum_matching,
+    minimum_weight_full_matching,
+    to_vertex_cover,
+)
+
+
+class TestMatching:
+    """Tests for bipartite matching algorithms."""
+
+    def setup_method(self):
+        """Creates a bipartite graph for use in testing matching algorithms.
+
+        The bipartite graph has a maximum cardinality matching that leaves
+        vertex 1 and vertex 10 unmatched. The first six numbers are the left
+        vertices and the next six numbers are the right vertices.
+
+        """
+        self.simple_graph = nx.complete_bipartite_graph(2, 3)
+        self.simple_solution = {0: 2, 1: 3, 2: 0, 3: 1}
+
+        edges = [(0, 7), (0, 8), (2, 6), (2, 9), (3, 8), (4, 8), (4, 9), (5, 11)]
+        self.top_nodes = set(range(6))
+        self.graph = nx.Graph()
+        self.graph.add_nodes_from(range(12))
+        self.graph.add_edges_from(edges)
+
+        # Example bipartite graph from issue 2127
+        G = nx.Graph()
+        G.add_nodes_from(
+            [
+                (1, "C"),
+                (1, "B"),
+                (0, "G"),
+                (1, "F"),
+                (1, "E"),
+                (0, "C"),
+                (1, "D"),
+                (1, "I"),
+                (0, "A"),
+                (0, "D"),
+                (0, "F"),
+                (0, "E"),
+                (0, "H"),
+                (1, "G"),
+                (1, "A"),
+                (0, "I"),
+                (0, "B"),
+                (1, "H"),
+            ]
+        )
+        G.add_edge((1, "C"), (0, "A"))
+        G.add_edge((1, "B"), (0, "A"))
+        G.add_edge((0, "G"), (1, "I"))
+        G.add_edge((0, "G"), (1, "H"))
+        G.add_edge((1, "F"), (0, "A"))
+        G.add_edge((1, "F"), (0, "C"))
+        G.add_edge((1, "F"), (0, "E"))
+        G.add_edge((1, "E"), (0, "A"))
+        G.add_edge((1, "E"), (0, "C"))
+        G.add_edge((0, "C"), (1, "D"))
+        G.add_edge((0, "C"), (1, "I"))
+        G.add_edge((0, "C"), (1, "G"))
+        G.add_edge((0, "C"), (1, "H"))
+        G.add_edge((1, "D"), (0, "A"))
+        G.add_edge((1, "I"), (0, "A"))
+        G.add_edge((1, "I"), (0, "E"))
+        G.add_edge((0, "A"), (1, "G"))
+        G.add_edge((0, "A"), (1, "H"))
+        G.add_edge((0, "E"), (1, "G"))
+        G.add_edge((0, "E"), (1, "H"))
+        self.disconnected_graph = G
+
+    def check_match(self, matching):
+        """Asserts that the matching is what we expect from the bipartite graph
+        constructed in the :meth:`setup` fixture.
+
+        """
+        # For the sake of brevity, rename `matching` to `M`.
+        M = matching
+        matched_vertices = frozenset(itertools.chain(*M.items()))
+        # Assert that the maximum number of vertices (10) is matched.
+        assert matched_vertices == frozenset(range(12)) - {1, 10}
+        # Assert that no vertex appears in two edges, or in other words, that
+        # the matching (u, v) and (v, u) both appear in the matching
+        # dictionary.
+        assert all(u == M[M[u]] for u in range(12) if u in M)
+
+    def check_vertex_cover(self, vertices):
+        """Asserts that the given set of vertices is the vertex cover we
+        expected from the bipartite graph constructed in the :meth:`setup`
+        fixture.
+
+        """
+        # By Konig's theorem, the number of edges in a maximum matching equals
+        # the number of vertices in a minimum vertex cover.
+        assert len(vertices) == 5
+        # Assert that the set is truly a vertex cover.
+        for u, v in self.graph.edges():
+            assert u in vertices or v in vertices
+        # TODO Assert that the vertices are the correct ones.
+
+    def test_eppstein_matching(self):
+        """Tests that David Eppstein's implementation of the Hopcroft--Karp
+        algorithm produces a maximum cardinality matching.
+
+        """
+        self.check_match(eppstein_matching(self.graph, self.top_nodes))
+
+    def test_hopcroft_karp_matching(self):
+        """Tests that the Hopcroft--Karp algorithm produces a maximum
+        cardinality matching in a bipartite graph.
+
+        """
+        self.check_match(hopcroft_karp_matching(self.graph, self.top_nodes))
+
+    def test_to_vertex_cover(self):
+        """Test for converting a maximum matching to a minimum vertex cover."""
+        matching = maximum_matching(self.graph, self.top_nodes)
+        vertex_cover = to_vertex_cover(self.graph, matching, self.top_nodes)
+        self.check_vertex_cover(vertex_cover)
+
+    def test_eppstein_matching_simple(self):
+        match = eppstein_matching(self.simple_graph)
+        assert match == self.simple_solution
+
+    def test_hopcroft_karp_matching_simple(self):
+        match = hopcroft_karp_matching(self.simple_graph)
+        assert match == self.simple_solution
+
+    def test_eppstein_matching_disconnected(self):
+        with pytest.raises(nx.AmbiguousSolution):
+            match = eppstein_matching(self.disconnected_graph)
+
+    def test_hopcroft_karp_matching_disconnected(self):
+        with pytest.raises(nx.AmbiguousSolution):
+            match = hopcroft_karp_matching(self.disconnected_graph)
+
+    def test_issue_2127(self):
+        """Test from issue 2127"""
+        # Build the example DAG
+        G = nx.DiGraph()
+        G.add_edge("A", "C")
+        G.add_edge("A", "B")
+        G.add_edge("C", "E")
+        G.add_edge("C", "D")
+        G.add_edge("E", "G")
+        G.add_edge("E", "F")
+        G.add_edge("G", "I")
+        G.add_edge("G", "H")
+
+        tc = nx.transitive_closure(G)
+        btc = nx.Graph()
+
+        # Create a bipartite graph based on the transitive closure of G
+        for v in tc.nodes():
+            btc.add_node((0, v))
+            btc.add_node((1, v))
+
+        for u, v in tc.edges():
+            btc.add_edge((0, u), (1, v))
+
+        top_nodes = {n for n in btc if n[0] == 0}
+        matching = hopcroft_karp_matching(btc, top_nodes)
+        vertex_cover = to_vertex_cover(btc, matching, top_nodes)
+        independent_set = set(G) - {v for _, v in vertex_cover}
+        assert {"B", "D", "F", "I", "H"} == independent_set
+
+    def test_vertex_cover_issue_2384(self):
+        G = nx.Graph([(0, 3), (1, 3), (1, 4), (2, 3)])
+        matching = maximum_matching(G)
+        vertex_cover = to_vertex_cover(G, matching)
+        for u, v in G.edges():
+            assert u in vertex_cover or v in vertex_cover
+
+    def test_vertex_cover_issue_3306(self):
+        G = nx.Graph()
+        edges = [(0, 2), (1, 0), (1, 1), (1, 2), (2, 2)]
+        G.add_edges_from([((i, "L"), (j, "R")) for i, j in edges])
+
+        matching = maximum_matching(G)
+        vertex_cover = to_vertex_cover(G, matching)
+        for u, v in G.edges():
+            assert u in vertex_cover or v in vertex_cover
+
+    def test_unorderable_nodes(self):
+        a = object()
+        b = object()
+        c = object()
+        d = object()
+        e = object()
+        G = nx.Graph([(a, d), (b, d), (b, e), (c, d)])
+        matching = maximum_matching(G)
+        vertex_cover = to_vertex_cover(G, matching)
+        for u, v in G.edges():
+            assert u in vertex_cover or v in vertex_cover
+
+
+def test_eppstein_matching():
+    """Test in accordance to issue #1927"""
+    G = nx.Graph()
+    G.add_nodes_from(["a", 2, 3, 4], bipartite=0)
+    G.add_nodes_from([1, "b", "c"], bipartite=1)
+    G.add_edges_from([("a", 1), ("a", "b"), (2, "b"), (2, "c"), (3, "c"), (4, 1)])
+    matching = eppstein_matching(G)
+    assert len(matching) == len(maximum_matching(G))
+    assert all(x in set(matching.keys()) for x in set(matching.values()))
+
+
+class TestMinimumWeightFullMatching:
+    @classmethod
+    def setup_class(cls):
+        pytest.importorskip("scipy")
+
+    def test_minimum_weight_full_matching_incomplete_graph(self):
+        B = nx.Graph()
+        B.add_nodes_from([1, 2], bipartite=0)
+        B.add_nodes_from([3, 4], bipartite=1)
+        B.add_edge(1, 4, weight=100)
+        B.add_edge(2, 3, weight=100)
+        B.add_edge(2, 4, weight=50)
+        matching = minimum_weight_full_matching(B)
+        assert matching == {1: 4, 2: 3, 4: 1, 3: 2}
+
+    def test_minimum_weight_full_matching_with_no_full_matching(self):
+        B = nx.Graph()
+        B.add_nodes_from([1, 2, 3], bipartite=0)
+        B.add_nodes_from([4, 5, 6], bipartite=1)
+        B.add_edge(1, 4, weight=100)
+        B.add_edge(2, 4, weight=100)
+        B.add_edge(3, 4, weight=50)
+        B.add_edge(3, 5, weight=50)
+        B.add_edge(3, 6, weight=50)
+        with pytest.raises(ValueError):
+            minimum_weight_full_matching(B)
+
+    def test_minimum_weight_full_matching_square(self):
+        G = nx.complete_bipartite_graph(3, 3)
+        G.add_edge(0, 3, weight=400)
+        G.add_edge(0, 4, weight=150)
+        G.add_edge(0, 5, weight=400)
+        G.add_edge(1, 3, weight=400)
+        G.add_edge(1, 4, weight=450)
+        G.add_edge(1, 5, weight=600)
+        G.add_edge(2, 3, weight=300)
+        G.add_edge(2, 4, weight=225)
+        G.add_edge(2, 5, weight=300)
+        matching = minimum_weight_full_matching(G)
+        assert matching == {0: 4, 1: 3, 2: 5, 4: 0, 3: 1, 5: 2}
+
+    def test_minimum_weight_full_matching_smaller_left(self):
+        G = nx.complete_bipartite_graph(3, 4)
+        G.add_edge(0, 3, weight=400)
+        G.add_edge(0, 4, weight=150)
+        G.add_edge(0, 5, weight=400)
+        G.add_edge(0, 6, weight=1)
+        G.add_edge(1, 3, weight=400)
+        G.add_edge(1, 4, weight=450)
+        G.add_edge(1, 5, weight=600)
+        G.add_edge(1, 6, weight=2)
+        G.add_edge(2, 3, weight=300)
+        G.add_edge(2, 4, weight=225)
+        G.add_edge(2, 5, weight=290)
+        G.add_edge(2, 6, weight=3)
+        matching = minimum_weight_full_matching(G)
+        assert matching == {0: 4, 1: 6, 2: 5, 4: 0, 5: 2, 6: 1}
+
+    def test_minimum_weight_full_matching_smaller_top_nodes_right(self):
+        G = nx.complete_bipartite_graph(3, 4)
+        G.add_edge(0, 3, weight=400)
+        G.add_edge(0, 4, weight=150)
+        G.add_edge(0, 5, weight=400)
+        G.add_edge(0, 6, weight=1)
+        G.add_edge(1, 3, weight=400)
+        G.add_edge(1, 4, weight=450)
+        G.add_edge(1, 5, weight=600)
+        G.add_edge(1, 6, weight=2)
+        G.add_edge(2, 3, weight=300)
+        G.add_edge(2, 4, weight=225)
+        G.add_edge(2, 5, weight=290)
+        G.add_edge(2, 6, weight=3)
+        matching = minimum_weight_full_matching(G, top_nodes=[3, 4, 5, 6])
+        assert matching == {0: 4, 1: 6, 2: 5, 4: 0, 5: 2, 6: 1}
+
+    def test_minimum_weight_full_matching_smaller_right(self):
+        G = nx.complete_bipartite_graph(4, 3)
+        G.add_edge(0, 4, weight=400)
+        G.add_edge(0, 5, weight=400)
+        G.add_edge(0, 6, weight=300)
+        G.add_edge(1, 4, weight=150)
+        G.add_edge(1, 5, weight=450)
+        G.add_edge(1, 6, weight=225)
+        G.add_edge(2, 4, weight=400)
+        G.add_edge(2, 5, weight=600)
+        G.add_edge(2, 6, weight=290)
+        G.add_edge(3, 4, weight=1)
+        G.add_edge(3, 5, weight=2)
+        G.add_edge(3, 6, weight=3)
+        matching = minimum_weight_full_matching(G)
+        assert matching == {1: 4, 2: 6, 3: 5, 4: 1, 5: 3, 6: 2}
+
+    def test_minimum_weight_full_matching_negative_weights(self):
+        G = nx.complete_bipartite_graph(2, 2)
+        G.add_edge(0, 2, weight=-2)
+        G.add_edge(0, 3, weight=0.2)
+        G.add_edge(1, 2, weight=-2)
+        G.add_edge(1, 3, weight=0.3)
+        matching = minimum_weight_full_matching(G)
+        assert matching == {0: 3, 1: 2, 2: 1, 3: 0}
+
+    def test_minimum_weight_full_matching_different_weight_key(self):
+        G = nx.complete_bipartite_graph(2, 2)
+        G.add_edge(0, 2, mass=2)
+        G.add_edge(0, 3, mass=0.2)
+        G.add_edge(1, 2, mass=1)
+        G.add_edge(1, 3, mass=2)
+        matching = minimum_weight_full_matching(G, weight="mass")
+        assert matching == {0: 3, 1: 2, 2: 1, 3: 0}

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/bipartite/tests/test_matrix.py

@@ -0,0 +1,79 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+sp = pytest.importorskip("scipy")
+sparse = pytest.importorskip("scipy.sparse")
+
+
+import networkx as nx
+from networkx.algorithms import bipartite
+from networkx.utils import edges_equal
+
+
+class TestBiadjacencyMatrix:
+    def test_biadjacency_matrix_weight(self):
+        G = nx.path_graph(5)
+        G.add_edge(0, 1, weight=2, other=4)
+        X = [1, 3]
+        Y = [0, 2, 4]
+        M = bipartite.biadjacency_matrix(G, X, weight="weight")
+        assert M[0, 0] == 2
+        M = bipartite.biadjacency_matrix(G, X, weight="other")
+        assert M[0, 0] == 4
+
+    def test_biadjacency_matrix(self):
+        tops = [2, 5, 10]
+        bots = [5, 10, 15]
+        for i in range(len(tops)):
+            G = bipartite.random_graph(tops[i], bots[i], 0.2)
+            top = [n for n, d in G.nodes(data=True) if d["bipartite"] == 0]
+            M = bipartite.biadjacency_matrix(G, top)
+            assert M.shape[0] == tops[i]
+            assert M.shape[1] == bots[i]
+
+    def test_biadjacency_matrix_order(self):
+        G = nx.path_graph(5)
+        G.add_edge(0, 1, weight=2)
+        X = [3, 1]
+        Y = [4, 2, 0]
+        M = bipartite.biadjacency_matrix(G, X, Y, weight="weight")
+        assert M[1, 2] == 2
+
+    def test_null_graph(self):
+        with pytest.raises(nx.NetworkXError):
+            bipartite.biadjacency_matrix(nx.Graph(), [])
+
+    def test_empty_graph(self):
+        with pytest.raises(nx.NetworkXError):
+            bipartite.biadjacency_matrix(nx.Graph([(1, 0)]), [])
+
+    def test_duplicate_row(self):
+        with pytest.raises(nx.NetworkXError):
+            bipartite.biadjacency_matrix(nx.Graph([(1, 0)]), [1, 1])
+
+    def test_duplicate_col(self):
+        with pytest.raises(nx.NetworkXError):
+            bipartite.biadjacency_matrix(nx.Graph([(1, 0)]), [0], [1, 1])
+
+    def test_format_keyword(self):
+        with pytest.raises(nx.NetworkXError):
+            bipartite.biadjacency_matrix(nx.Graph([(1, 0)]), [0], format="foo")
+
+    def test_from_biadjacency_roundtrip(self):
+        B1 = nx.path_graph(5)
+        M = bipartite.biadjacency_matrix(B1, [0, 2, 4])
+        B2 = bipartite.from_biadjacency_matrix(M)
+        assert nx.is_isomorphic(B1, B2)
+
+    def test_from_biadjacency_weight(self):
+        M = sparse.csc_matrix([[1, 2], [0, 3]])
+        B = bipartite.from_biadjacency_matrix(M)
+        assert edges_equal(B.edges(), [(0, 2), (0, 3), (1, 3)])
+        B = bipartite.from_biadjacency_matrix(M, edge_attribute="weight")
+        e = [(0, 2, {"weight": 1}), (0, 3, {"weight": 2}), (1, 3, {"weight": 3})]
+        assert edges_equal(B.edges(data=True), e)
+
+    def test_from_biadjacency_multigraph(self):
+        M = sparse.csc_matrix([[1, 2], [0, 3]])
+        B = bipartite.from_biadjacency_matrix(M, create_using=nx.MultiGraph())
+        assert edges_equal(B.edges(), [(0, 2), (0, 3), (0, 3), (1, 3), (1, 3), (1, 3)])

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/bipartite/tests/test_redundancy.py

@@ -0,0 +1,37 @@
+"""Unit tests for the :mod:`networkx.algorithms.bipartite.redundancy` module.
+
+"""
+
+import pytest
+
+from networkx import NetworkXError, cycle_graph
+from networkx.algorithms.bipartite import complete_bipartite_graph, node_redundancy
+
+
+def test_no_redundant_nodes():
+    G = complete_bipartite_graph(2, 2)
+
+    # when nodes is None
+    rc = node_redundancy(G)
+    assert all(redundancy == 1 for redundancy in rc.values())
+
+    # when set of nodes is specified
+    rc = node_redundancy(G, (2, 3))
+    assert rc == {2: 1.0, 3: 1.0}
+
+
+def test_redundant_nodes():
+    G = cycle_graph(6)
+    edge = {0, 3}
+    G.add_edge(*edge)
+    redundancy = node_redundancy(G)
+    for v in edge:
+        assert redundancy[v] == 2 / 3
+    for v in set(G) - edge:
+        assert redundancy[v] == 1
+
+
+def test_not_enough_neighbors():
+    with pytest.raises(NetworkXError):
+        G = complete_bipartite_graph(1, 2)
+        node_redundancy(G)

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/community/tests/test_lukes.py

@@ -0,0 +1,152 @@
+from itertools import product
+
+import pytest
+
+import networkx as nx
+
+EWL = "e_weight"
+NWL = "n_weight"
+
+
+# first test from the Lukes original paper
+def paper_1_case(float_edge_wt=False, explicit_node_wt=True, directed=False):
+    # problem-specific constants
+    limit = 3
+
+    # configuration
+    if float_edge_wt:
+        shift = 0.001
+    else:
+        shift = 0
+
+    if directed:
+        example_1 = nx.DiGraph()
+    else:
+        example_1 = nx.Graph()
+
+    # graph creation
+    example_1.add_edge(1, 2, **{EWL: 3 + shift})
+    example_1.add_edge(1, 4, **{EWL: 2 + shift})
+    example_1.add_edge(2, 3, **{EWL: 4 + shift})
+    example_1.add_edge(2, 5, **{EWL: 6 + shift})
+
+    # node weights
+    if explicit_node_wt:
+        nx.set_node_attributes(example_1, 1, NWL)
+        wtu = NWL
+    else:
+        wtu = None
+
+    # partitioning
+    clusters_1 = {
+        frozenset(x)
+        for x in nx.community.lukes_partitioning(
+            example_1, limit, node_weight=wtu, edge_weight=EWL
+        )
+    }
+
+    return clusters_1
+
+
+# second test from the Lukes original paper
+def paper_2_case(explicit_edge_wt=True, directed=False):
+    # problem specific constants
+    byte_block_size = 32
+
+    # configuration
+    if directed:
+        example_2 = nx.DiGraph()
+    else:
+        example_2 = nx.Graph()
+
+    if explicit_edge_wt:
+        edic = {EWL: 1}
+        wtu = EWL
+    else:
+        edic = {}
+        wtu = None
+
+    # graph creation
+    example_2.add_edge("name", "home_address", **edic)
+    example_2.add_edge("name", "education", **edic)
+    example_2.add_edge("education", "bs", **edic)
+    example_2.add_edge("education", "ms", **edic)
+    example_2.add_edge("education", "phd", **edic)
+    example_2.add_edge("name", "telephone", **edic)
+    example_2.add_edge("telephone", "home", **edic)
+    example_2.add_edge("telephone", "office", **edic)
+    example_2.add_edge("office", "no1", **edic)
+    example_2.add_edge("office", "no2", **edic)
+
+    example_2.nodes["name"][NWL] = 20
+    example_2.nodes["education"][NWL] = 10
+    example_2.nodes["bs"][NWL] = 1
+    example_2.nodes["ms"][NWL] = 1
+    example_2.nodes["phd"][NWL] = 1
+    example_2.nodes["home_address"][NWL] = 8
+    example_2.nodes["telephone"][NWL] = 8
+    example_2.nodes["home"][NWL] = 8
+    example_2.nodes["office"][NWL] = 4
+    example_2.nodes["no1"][NWL] = 1
+    example_2.nodes["no2"][NWL] = 1
+
+    # partitioning
+    clusters_2 = {
+        frozenset(x)
+        for x in nx.community.lukes_partitioning(
+            example_2, byte_block_size, node_weight=NWL, edge_weight=wtu
+        )
+    }
+
+    return clusters_2
+
+
+def test_paper_1_case():
+    ground_truth = {frozenset([1, 4]), frozenset([2, 3, 5])}
+
+    tf = (True, False)
+    for flt, nwt, drc in product(tf, tf, tf):
+        part = paper_1_case(flt, nwt, drc)
+        assert part == ground_truth
+
+
+def test_paper_2_case():
+    ground_truth = {
+        frozenset(["education", "bs", "ms", "phd"]),
+        frozenset(["name", "home_address"]),
+        frozenset(["telephone", "home", "office", "no1", "no2"]),
+    }
+
+    tf = (True, False)
+    for ewt, drc in product(tf, tf):
+        part = paper_2_case(ewt, drc)
+        assert part == ground_truth
+
+
+def test_mandatory_tree():
+    not_a_tree = nx.complete_graph(4)
+
+    with pytest.raises(nx.NotATree):
+        nx.community.lukes_partitioning(not_a_tree, 5)
+
+
+def test_mandatory_integrality():
+    byte_block_size = 32
+
+    ex_1_broken = nx.DiGraph()
+
+    ex_1_broken.add_edge(1, 2, **{EWL: 3.2})
+    ex_1_broken.add_edge(1, 4, **{EWL: 2.4})
+    ex_1_broken.add_edge(2, 3, **{EWL: 4.0})
+    ex_1_broken.add_edge(2, 5, **{EWL: 6.3})
+
+    ex_1_broken.nodes[1][NWL] = 1.2  # !
+    ex_1_broken.nodes[2][NWL] = 1
+    ex_1_broken.nodes[3][NWL] = 1
+    ex_1_broken.nodes[4][NWL] = 1
+    ex_1_broken.nodes[5][NWL] = 2
+
+    with pytest.raises(TypeError):
+        nx.community.lukes_partitioning(
+            ex_1_broken, byte_block_size, node_weight=NWL, edge_weight=EWL
+        )

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/community/tests/test_quality.py

@@ -0,0 +1,138 @@
+"""Unit tests for the :mod:`networkx.algorithms.community.quality`
+module.
+
+"""
+import pytest
+
+import networkx as nx
+from networkx import barbell_graph
+from networkx.algorithms.community import modularity, partition_quality
+from networkx.algorithms.community.quality import inter_community_edges
+
+
+class TestPerformance:
+    """Unit tests for the :func:`performance` function."""
+
+    def test_bad_partition(self):
+        """Tests that a poor partition has a low performance measure."""
+        G = barbell_graph(3, 0)
+        partition = [{0, 1, 4}, {2, 3, 5}]
+        assert 8 / 15 == pytest.approx(partition_quality(G, partition)[1], abs=1e-7)
+
+    def test_good_partition(self):
+        """Tests that a good partition has a high performance measure."""
+        G = barbell_graph(3, 0)
+        partition = [{0, 1, 2}, {3, 4, 5}]
+        assert 14 / 15 == pytest.approx(partition_quality(G, partition)[1], abs=1e-7)
+
+
+class TestCoverage:
+    """Unit tests for the :func:`coverage` function."""
+
+    def test_bad_partition(self):
+        """Tests that a poor partition has a low coverage measure."""
+        G = barbell_graph(3, 0)
+        partition = [{0, 1, 4}, {2, 3, 5}]
+        assert 3 / 7 == pytest.approx(partition_quality(G, partition)[0], abs=1e-7)
+
+    def test_good_partition(self):
+        """Tests that a good partition has a high coverage measure."""
+        G = barbell_graph(3, 0)
+        partition = [{0, 1, 2}, {3, 4, 5}]
+        assert 6 / 7 == pytest.approx(partition_quality(G, partition)[0], abs=1e-7)
+
+
+def test_modularity():
+    G = nx.barbell_graph(3, 0)
+    C = [{0, 1, 4}, {2, 3, 5}]
+    assert (-16 / (14**2)) == pytest.approx(modularity(G, C), abs=1e-7)
+    C = [{0, 1, 2}, {3, 4, 5}]
+    assert (35 * 2) / (14**2) == pytest.approx(modularity(G, C), abs=1e-7)
+
+    n = 1000
+    G = nx.erdos_renyi_graph(n, 0.09, seed=42, directed=True)
+    C = [set(range(n // 2)), set(range(n // 2, n))]
+    assert 0.00017154251389292754 == pytest.approx(modularity(G, C), abs=1e-7)
+
+    G = nx.margulis_gabber_galil_graph(10)
+    mid_value = G.number_of_nodes() // 2
+    nodes = list(G.nodes)
+    C = [set(nodes[:mid_value]), set(nodes[mid_value:])]
+    assert 0.13 == pytest.approx(modularity(G, C), abs=1e-7)
+
+    G = nx.DiGraph()
+    G.add_edges_from([(2, 1), (2, 3), (3, 4)])
+    C = [{1, 2}, {3, 4}]
+    assert 2 / 9 == pytest.approx(modularity(G, C), abs=1e-7)
+
+
+def test_modularity_resolution():
+    G = nx.barbell_graph(3, 0)
+    C = [{0, 1, 4}, {2, 3, 5}]
+    assert modularity(G, C) == pytest.approx(3 / 7 - 100 / 14**2)
+    gamma = 2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx(3 / 7 - gamma * 100 / 14**2)
+    gamma = 0.2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx(3 / 7 - gamma * 100 / 14**2)
+
+    C = [{0, 1, 2}, {3, 4, 5}]
+    assert modularity(G, C) == pytest.approx(6 / 7 - 98 / 14**2)
+    gamma = 2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx(6 / 7 - gamma * 98 / 14**2)
+    gamma = 0.2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx(6 / 7 - gamma * 98 / 14**2)
+
+    G = nx.barbell_graph(5, 3)
+    C = [frozenset(range(5)), frozenset(range(8, 13)), frozenset(range(5, 8))]
+    gamma = 1
+    result = modularity(G, C, resolution=gamma)
+    # This C is maximal for gamma=1:  modularity = 0.518229
+    assert result == pytest.approx((22 / 24) - gamma * (918 / (48**2)))
+    gamma = 2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((22 / 24) - gamma * (918 / (48**2)))
+    gamma = 0.2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((22 / 24) - gamma * (918 / (48**2)))
+
+    C = [{0, 1, 2, 3}, {9, 10, 11, 12}, {5, 6, 7}, {4}, {8}]
+    gamma = 1
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((14 / 24) - gamma * (598 / (48**2)))
+    gamma = 2.5
+    result = modularity(G, C, resolution=gamma)
+    # This C is maximal for gamma=2.5:  modularity = -0.06553819
+    assert result == pytest.approx((14 / 24) - gamma * (598 / (48**2)))
+    gamma = 0.2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((14 / 24) - gamma * (598 / (48**2)))
+
+    C = [frozenset(range(8)), frozenset(range(8, 13))]
+    gamma = 1
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((23 / 24) - gamma * (1170 / (48**2)))
+    gamma = 2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((23 / 24) - gamma * (1170 / (48**2)))
+    gamma = 0.3
+    result = modularity(G, C, resolution=gamma)
+    # This C is maximal for gamma=0.3:  modularity = 0.805990
+    assert result == pytest.approx((23 / 24) - gamma * (1170 / (48**2)))
+
+
+def test_inter_community_edges_with_digraphs():
+    G = nx.complete_graph(2, create_using=nx.DiGraph())
+    partition = [{0}, {1}]
+    assert inter_community_edges(G, partition) == 2
+
+    G = nx.complete_graph(10, create_using=nx.DiGraph())
+    partition = [{0}, {1, 2}, {3, 4, 5}, {6, 7, 8, 9}]
+    assert inter_community_edges(G, partition) == 70
+
+    G = nx.cycle_graph(4, create_using=nx.DiGraph())
+    partition = [{0, 1}, {2, 3}]
+    assert inter_community_edges(G, partition) == 2

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/components/__init__.py

@@ -0,0 +1,6 @@
+from .connected import *
+from .strongly_connected import *
+from .weakly_connected import *
+from .attracting import *
+from .biconnected import *
+from .semiconnected import *

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/components/biconnected.py

@@ -0,0 +1,393 @@
+"""Biconnected components and articulation points."""
+from itertools import chain
+
+import networkx as nx
+from networkx.utils.decorators import not_implemented_for
+
+__all__ = [
+    "biconnected_components",
+    "biconnected_component_edges",
+    "is_biconnected",
+    "articulation_points",
+]
+
+
+@not_implemented_for("directed")
+@nx._dispatch
+def is_biconnected(G):
+    """Returns True if the graph is biconnected, False otherwise.
+
+    A graph is biconnected if, and only if, it cannot be disconnected by
+    removing only one node (and all edges incident on that node).  If
+    removing a node increases the number of disconnected components
+    in the graph, that node is called an articulation point, or cut
+    vertex.  A biconnected graph has no articulation points.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        An undirected graph.
+
+    Returns
+    -------
+    biconnected : bool
+        True if the graph is biconnected, False otherwise.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is not undirected.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> print(nx.is_biconnected(G))
+    False
+    >>> G.add_edge(0, 3)
+    >>> print(nx.is_biconnected(G))
+    True
+
+    See Also
+    --------
+    biconnected_components
+    articulation_points
+    biconnected_component_edges
+    is_strongly_connected
+    is_weakly_connected
+    is_connected
+    is_semiconnected
+
+    Notes
+    -----
+    The algorithm to find articulation points and biconnected
+    components is implemented using a non-recursive depth-first-search
+    (DFS) that keeps track of the highest level that back edges reach
+    in the DFS tree.  A node `n` is an articulation point if, and only
+    if, there exists a subtree rooted at `n` such that there is no
+    back edge from any successor of `n` that links to a predecessor of
+    `n` in the DFS tree.  By keeping track of all the edges traversed
+    by the DFS we can obtain the biconnected components because all
+    edges of a bicomponent will be traversed consecutively between
+    articulation points.
+
+    References
+    ----------
+    .. [1] Hopcroft, J.; Tarjan, R. (1973).
+       "Efficient algorithms for graph manipulation".
+       Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
+
+    """
+    bccs = biconnected_components(G)
+    try:
+        bcc = next(bccs)
+    except StopIteration:
+        # No bicomponents (empty graph?)
+        return False
+    try:
+        next(bccs)
+    except StopIteration:
+        # Only one bicomponent
+        return len(bcc) == len(G)
+    else:
+        # Multiple bicomponents
+        return False
+
+
+@not_implemented_for("directed")
+@nx._dispatch
+def biconnected_component_edges(G):
+    """Returns a generator of lists of edges, one list for each biconnected
+    component of the input graph.
+
+    Biconnected components are maximal subgraphs such that the removal of a
+    node (and all edges incident on that node) will not disconnect the
+    subgraph.  Note that nodes may be part of more than one biconnected
+    component.  Those nodes are articulation points, or cut vertices.
+    However, each edge belongs to one, and only one, biconnected component.
+
+    Notice that by convention a dyad is considered a biconnected component.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        An undirected graph.
+
+    Returns
+    -------
+    edges : generator of lists
+        Generator of lists of edges, one list for each bicomponent.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is not undirected.
+
+    Examples
+    --------
+    >>> G = nx.barbell_graph(4, 2)
+    >>> print(nx.is_biconnected(G))
+    False
+    >>> bicomponents_edges = list(nx.biconnected_component_edges(G))
+    >>> len(bicomponents_edges)
+    5
+    >>> G.add_edge(2, 8)
+    >>> print(nx.is_biconnected(G))
+    True
+    >>> bicomponents_edges = list(nx.biconnected_component_edges(G))
+    >>> len(bicomponents_edges)
+    1
+
+    See Also
+    --------
+    is_biconnected,
+    biconnected_components,
+    articulation_points,
+
+    Notes
+    -----
+    The algorithm to find articulation points and biconnected
+    components is implemented using a non-recursive depth-first-search
+    (DFS) that keeps track of the highest level that back edges reach
+    in the DFS tree.  A node `n` is an articulation point if, and only
+    if, there exists a subtree rooted at `n` such that there is no
+    back edge from any successor of `n` that links to a predecessor of
+    `n` in the DFS tree.  By keeping track of all the edges traversed
+    by the DFS we can obtain the biconnected components because all
+    edges of a bicomponent will be traversed consecutively between
+    articulation points.
+
+    References
+    ----------
+    .. [1] Hopcroft, J.; Tarjan, R. (1973).
+           "Efficient algorithms for graph manipulation".
+           Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
+
+    """
+    yield from _biconnected_dfs(G, components=True)
+
+
+@not_implemented_for("directed")
+@nx._dispatch
+def biconnected_components(G):
+    """Returns a generator of sets of nodes, one set for each biconnected
+    component of the graph
+
+    Biconnected components are maximal subgraphs such that the removal of a
+    node (and all edges incident on that node) will not disconnect the
+    subgraph. Note that nodes may be part of more than one biconnected
+    component.  Those nodes are articulation points, or cut vertices.  The
+    removal of articulation points will increase the number of connected
+    components of the graph.
+
+    Notice that by convention a dyad is considered a biconnected component.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        An undirected graph.
+
+    Returns
+    -------
+    nodes : generator
+        Generator of sets of nodes, one set for each biconnected component.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is not undirected.
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(5, 1)
+    >>> print(nx.is_biconnected(G))
+    False
+    >>> bicomponents = list(nx.biconnected_components(G))
+    >>> len(bicomponents)
+    2
+    >>> G.add_edge(0, 5)
+    >>> print(nx.is_biconnected(G))
+    True
+    >>> bicomponents = list(nx.biconnected_components(G))
+    >>> len(bicomponents)
+    1
+
+    You can generate a sorted list of biconnected components, largest
+    first, using sort.
+
+    >>> G.remove_edge(0, 5)
+    >>> [len(c) for c in sorted(nx.biconnected_components(G), key=len, reverse=True)]
+    [5, 2]
+
+    If you only want the largest connected component, it's more
+    efficient to use max instead of sort.
+
+    >>> Gc = max(nx.biconnected_components(G), key=len)
+
+    To create the components as subgraphs use:
+    ``(G.subgraph(c).copy() for c in biconnected_components(G))``
+
+    See Also
+    --------
+    is_biconnected
+    articulation_points
+    biconnected_component_edges
+    k_components : this function is a special case where k=2
+    bridge_components : similar to this function, but is defined using
+        2-edge-connectivity instead of 2-node-connectivity.
+
+    Notes
+    -----
+    The algorithm to find articulation points and biconnected
+    components is implemented using a non-recursive depth-first-search
+    (DFS) that keeps track of the highest level that back edges reach
+    in the DFS tree.  A node `n` is an articulation point if, and only
+    if, there exists a subtree rooted at `n` such that there is no
+    back edge from any successor of `n` that links to a predecessor of
+    `n` in the DFS tree.  By keeping track of all the edges traversed
+    by the DFS we can obtain the biconnected components because all
+    edges of a bicomponent will be traversed consecutively between
+    articulation points.
+
+    References
+    ----------
+    .. [1] Hopcroft, J.; Tarjan, R. (1973).
+           "Efficient algorithms for graph manipulation".
+           Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
+
+    """
+    for comp in _biconnected_dfs(G, components=True):
+        yield set(chain.from_iterable(comp))
+
+
+@not_implemented_for("directed")
+@nx._dispatch
+def articulation_points(G):
+    """Yield the articulation points, or cut vertices, of a graph.
+
+    An articulation point or cut vertex is any node whose removal (along with
+    all its incident edges) increases the number of connected components of
+    a graph.  An undirected connected graph without articulation points is
+    biconnected. Articulation points belong to more than one biconnected
+    component of a graph.
+
+    Notice that by convention a dyad is considered a biconnected component.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        An undirected graph.
+
+    Yields
+    ------
+    node
+        An articulation point in the graph.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is not undirected.
+
+    Examples
+    --------
+
+    >>> G = nx.barbell_graph(4, 2)
+    >>> print(nx.is_biconnected(G))
+    False
+    >>> len(list(nx.articulation_points(G)))
+    4
+    >>> G.add_edge(2, 8)
+    >>> print(nx.is_biconnected(G))
+    True
+    >>> len(list(nx.articulation_points(G)))
+    0
+
+    See Also
+    --------
+    is_biconnected
+    biconnected_components
+    biconnected_component_edges
+
+    Notes
+    -----
+    The algorithm to find articulation points and biconnected
+    components is implemented using a non-recursive depth-first-search
+    (DFS) that keeps track of the highest level that back edges reach
+    in the DFS tree.  A node `n` is an articulation point if, and only
+    if, there exists a subtree rooted at `n` such that there is no
+    back edge from any successor of `n` that links to a predecessor of
+    `n` in the DFS tree.  By keeping track of all the edges traversed
+    by the DFS we can obtain the biconnected components because all
+    edges of a bicomponent will be traversed consecutively between
+    articulation points.
+
+    References
+    ----------
+    .. [1] Hopcroft, J.; Tarjan, R. (1973).
+           "Efficient algorithms for graph manipulation".
+           Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
+
+    """
+    seen = set()
+    for articulation in _biconnected_dfs(G, components=False):
+        if articulation not in seen:
+            seen.add(articulation)
+            yield articulation
+
+
+@not_implemented_for("directed")
+def _biconnected_dfs(G, components=True):
+    # depth-first search algorithm to generate articulation points
+    # and biconnected components
+    visited = set()
+    for start in G:
+        if start in visited:
+            continue
+        discovery = {start: 0}  # time of first discovery of node during search
+        low = {start: 0}
+        root_children = 0
+        visited.add(start)
+        edge_stack = []
+        stack = [(start, start, iter(G[start]))]
+        edge_index = {}
+        while stack:
+            grandparent, parent, children = stack[-1]
+            try:
+                child = next(children)
+                if grandparent == child:
+                    continue
+                if child in visited:
+                    if discovery[child] <= discovery[parent]:  # back edge
+                        low[parent] = min(low[parent], discovery[child])
+                        if components:
+                            edge_index[parent, child] = len(edge_stack)
+                            edge_stack.append((parent, child))
+                else:
+                    low[child] = discovery[child] = len(discovery)
+                    visited.add(child)
+                    stack.append((parent, child, iter(G[child])))
+                    if components:
+                        edge_index[parent, child] = len(edge_stack)
+                        edge_stack.append((parent, child))
+
+            except StopIteration:
+                stack.pop()
+                if len(stack) > 1:
+                    if low[parent] >= discovery[grandparent]:
+                        if components:
+                            ind = edge_index[grandparent, parent]
+                            yield edge_stack[ind:]
+                            del edge_stack[ind:]
+
+                        else:
+                            yield grandparent
+                    low[grandparent] = min(low[parent], low[grandparent])
+                elif stack:  # length 1 so grandparent is root
+                    root_children += 1
+                    if components:
+                        ind = edge_index[grandparent, parent]
+                        yield edge_stack[ind:]
+                        del edge_stack[ind:]
+        if not components:
+            # root node is articulation point if it has more than 1 child
+            if root_children > 1:
+                yield start

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/components/tests/test_biconnected.py

@@ -0,0 +1,248 @@
+import pytest
+
+import networkx as nx
+from networkx import NetworkXNotImplemented
+
+
+def assert_components_edges_equal(x, y):
+    sx = {frozenset(frozenset(e) for e in c) for c in x}
+    sy = {frozenset(frozenset(e) for e in c) for c in y}
+    assert sx == sy
+
+
+def assert_components_equal(x, y):
+    sx = {frozenset(c) for c in x}
+    sy = {frozenset(c) for c in y}
+    assert sx == sy
+
+
+def test_barbell():
+    G = nx.barbell_graph(8, 4)
+    nx.add_path(G, [7, 20, 21, 22])
+    nx.add_cycle(G, [22, 23, 24, 25])
+    pts = set(nx.articulation_points(G))
+    assert pts == {7, 8, 9, 10, 11, 12, 20, 21, 22}
+
+    answer = [
+        {12, 13, 14, 15, 16, 17, 18, 19},
+        {0, 1, 2, 3, 4, 5, 6, 7},
+        {22, 23, 24, 25},
+        {11, 12},
+        {10, 11},
+        {9, 10},
+        {8, 9},
+        {7, 8},
+        {21, 22},
+        {20, 21},
+        {7, 20},
+    ]
+    assert_components_equal(list(nx.biconnected_components(G)), answer)
+
+    G.add_edge(2, 17)
+    pts = set(nx.articulation_points(G))
+    assert pts == {7, 20, 21, 22}
+
+
+def test_articulation_points_repetitions():
+    G = nx.Graph()
+    G.add_edges_from([(0, 1), (1, 2), (1, 3)])
+    assert list(nx.articulation_points(G)) == [1]
+
+
+def test_articulation_points_cycle():
+    G = nx.cycle_graph(3)
+    nx.add_cycle(G, [1, 3, 4])
+    pts = set(nx.articulation_points(G))
+    assert pts == {1}
+
+
+def test_is_biconnected():
+    G = nx.cycle_graph(3)
+    assert nx.is_biconnected(G)
+    nx.add_cycle(G, [1, 3, 4])
+    assert not nx.is_biconnected(G)
+
+
+def test_empty_is_biconnected():
+    G = nx.empty_graph(5)
+    assert not nx.is_biconnected(G)
+    G.add_edge(0, 1)
+    assert not nx.is_biconnected(G)
+
+
+def test_biconnected_components_cycle():
+    G = nx.cycle_graph(3)
+    nx.add_cycle(G, [1, 3, 4])
+    answer = [{0, 1, 2}, {1, 3, 4}]
+    assert_components_equal(list(nx.biconnected_components(G)), answer)
+
+
+def test_biconnected_components1():
+    # graph example from
+    # https://web.archive.org/web/20121229123447/http://www.ibluemojo.com/school/articul_algorithm.html
+    edges = [
+        (0, 1),
+        (0, 5),
+        (0, 6),
+        (0, 14),
+        (1, 5),
+        (1, 6),
+        (1, 14),
+        (2, 4),
+        (2, 10),
+        (3, 4),
+        (3, 15),
+        (4, 6),
+        (4, 7),
+        (4, 10),
+        (5, 14),
+        (6, 14),
+        (7, 9),
+        (8, 9),
+        (8, 12),
+        (8, 13),
+        (10, 15),
+        (11, 12),
+        (11, 13),
+        (12, 13),
+    ]
+    G = nx.Graph(edges)
+    pts = set(nx.articulation_points(G))
+    assert pts == {4, 6, 7, 8, 9}
+    comps = list(nx.biconnected_component_edges(G))
+    answer = [
+        [(3, 4), (15, 3), (10, 15), (10, 4), (2, 10), (4, 2)],
+        [(13, 12), (13, 8), (11, 13), (12, 11), (8, 12)],
+        [(9, 8)],
+        [(7, 9)],
+        [(4, 7)],
+        [(6, 4)],
+        [(14, 0), (5, 1), (5, 0), (14, 5), (14, 1), (6, 14), (6, 0), (1, 6), (0, 1)],
+    ]
+    assert_components_edges_equal(comps, answer)
+
+
+def test_biconnected_components2():
+    G = nx.Graph()
+    nx.add_cycle(G, "ABC")
+    nx.add_cycle(G, "CDE")
+    nx.add_cycle(G, "FIJHG")
+    nx.add_cycle(G, "GIJ")
+    G.add_edge("E", "G")
+    comps = list(nx.biconnected_component_edges(G))
+    answer = [
+        [
+            tuple("GF"),
+            tuple("FI"),
+            tuple("IG"),
+            tuple("IJ"),
+            tuple("JG"),
+            tuple("JH"),
+            tuple("HG"),
+        ],
+        [tuple("EG")],
+        [tuple("CD"), tuple("DE"), tuple("CE")],
+        [tuple("AB"), tuple("BC"), tuple("AC")],
+    ]
+    assert_components_edges_equal(comps, answer)
+
+
+def test_biconnected_davis():
+    D = nx.davis_southern_women_graph()
+    bcc = list(nx.biconnected_components(D))[0]
+    assert set(D) == bcc  # All nodes in a giant bicomponent
+    # So no articulation points
+    assert len(list(nx.articulation_points(D))) == 0
+
+
+def test_biconnected_karate():
+    K = nx.karate_club_graph()
+    answer = [
+        {
+            0,
+            1,
+            2,
+            3,
+            7,
+            8,
+            9,
+            12,
+            13,
+            14,
+            15,
+            17,
+            18,
+            19,
+            20,
+            21,
+            22,
+            23,
+            24,
+            25,
+            26,
+            27,
+            28,
+            29,
+            30,
+            31,
+            32,
+            33,
+        },
+        {0, 4, 5, 6, 10, 16},
+        {0, 11},
+    ]
+    bcc = list(nx.biconnected_components(K))
+    assert_components_equal(bcc, answer)
+    assert set(nx.articulation_points(K)) == {0}
+
+
+def test_biconnected_eppstein():
+    # tests from http://www.ics.uci.edu/~eppstein/PADS/Biconnectivity.py
+    G1 = nx.Graph(
+        {
+            0: [1, 2, 5],
+            1: [0, 5],
+            2: [0, 3, 4],
+            3: [2, 4, 5, 6],
+            4: [2, 3, 5, 6],
+            5: [0, 1, 3, 4],
+            6: [3, 4],
+        }
+    )
+    G2 = nx.Graph(
+        {
+            0: [2, 5],
+            1: [3, 8],
+            2: [0, 3, 5],
+            3: [1, 2, 6, 8],
+            4: [7],
+            5: [0, 2],
+            6: [3, 8],
+            7: [4],
+            8: [1, 3, 6],
+        }
+    )
+    assert nx.is_biconnected(G1)
+    assert not nx.is_biconnected(G2)
+    answer_G2 = [{1, 3, 6, 8}, {0, 2, 5}, {2, 3}, {4, 7}]
+    bcc = list(nx.biconnected_components(G2))
+    assert_components_equal(bcc, answer_G2)
+
+
+def test_null_graph():
+    G = nx.Graph()
+    assert not nx.is_biconnected(G)
+    assert list(nx.biconnected_components(G)) == []
+    assert list(nx.biconnected_component_edges(G)) == []
+    assert list(nx.articulation_points(G)) == []
+
+
+def test_connected_raise():
+    DG = nx.DiGraph()
+    with pytest.raises(NetworkXNotImplemented):
+        next(nx.biconnected_components(DG))
+    with pytest.raises(NetworkXNotImplemented):
+        next(nx.biconnected_component_edges(DG))
+    with pytest.raises(NetworkXNotImplemented):
+        next(nx.articulation_points(DG))
+    pytest.raises(NetworkXNotImplemented, nx.is_biconnected, DG)

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/components/tests/test_connected.py

@@ -0,0 +1,117 @@
+import pytest
+
+import networkx as nx
+from networkx import NetworkXNotImplemented
+from networkx import convert_node_labels_to_integers as cnlti
+from networkx.classes.tests import dispatch_interface
+
+
+class TestConnected:
+    @classmethod
+    def setup_class(cls):
+        G1 = cnlti(nx.grid_2d_graph(2, 2), first_label=0, ordering="sorted")
+        G2 = cnlti(nx.lollipop_graph(3, 3), first_label=4, ordering="sorted")
+        G3 = cnlti(nx.house_graph(), first_label=10, ordering="sorted")
+        cls.G = nx.union(G1, G2)
+        cls.G = nx.union(cls.G, G3)
+        cls.DG = nx.DiGraph([(1, 2), (1, 3), (2, 3)])
+        cls.grid = cnlti(nx.grid_2d_graph(4, 4), first_label=1)
+
+        cls.gc = []
+        G = nx.DiGraph()
+        G.add_edges_from(
+            [
+                (1, 2),
+                (2, 3),
+                (2, 8),
+                (3, 4),
+                (3, 7),
+                (4, 5),
+                (5, 3),
+                (5, 6),
+                (7, 4),
+                (7, 6),
+                (8, 1),
+                (8, 7),
+            ]
+        )
+        C = [[3, 4, 5, 7], [1, 2, 8], [6]]
+        cls.gc.append((G, C))
+
+        G = nx.DiGraph()
+        G.add_edges_from([(1, 2), (1, 3), (1, 4), (4, 2), (3, 4), (2, 3)])
+        C = [[2, 3, 4], [1]]
+        cls.gc.append((G, C))
+
+        G = nx.DiGraph()
+        G.add_edges_from([(1, 2), (2, 3), (3, 2), (2, 1)])
+        C = [[1, 2, 3]]
+        cls.gc.append((G, C))
+
+        # Eppstein's tests
+        G = nx.DiGraph({0: [1], 1: [2, 3], 2: [4, 5], 3: [4, 5], 4: [6], 5: [], 6: []})
+        C = [[0], [1], [2], [3], [4], [5], [6]]
+        cls.gc.append((G, C))
+
+        G = nx.DiGraph({0: [1], 1: [2, 3, 4], 2: [0, 3], 3: [4], 4: [3]})
+        C = [[0, 1, 2], [3, 4]]
+        cls.gc.append((G, C))
+
+        G = nx.DiGraph()
+        C = []
+        cls.gc.append((G, C))
+
+    # This additionally tests the @nx._dispatch mechanism, treating
+    # nx.connected_components as if it were a re-implementation from another package
+    @pytest.mark.parametrize("wrapper", [lambda x: x, dispatch_interface.convert])
+    def test_connected_components(self, wrapper):
+        cc = nx.connected_components
+        G = wrapper(self.G)
+        C = {
+            frozenset([0, 1, 2, 3]),
+            frozenset([4, 5, 6, 7, 8, 9]),
+            frozenset([10, 11, 12, 13, 14]),
+        }
+        assert {frozenset(g) for g in cc(G)} == C
+
+    def test_number_connected_components(self):
+        ncc = nx.number_connected_components
+        assert ncc(self.G) == 3
+
+    def test_number_connected_components2(self):
+        ncc = nx.number_connected_components
+        assert ncc(self.grid) == 1
+
+    def test_connected_components2(self):
+        cc = nx.connected_components
+        G = self.grid
+        C = {frozenset([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])}
+        assert {frozenset(g) for g in cc(G)} == C
+
+    def test_node_connected_components(self):
+        ncc = nx.node_connected_component
+        G = self.grid
+        C = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}
+        assert ncc(G, 1) == C
+
+    def test_is_connected(self):
+        assert nx.is_connected(self.grid)
+        G = nx.Graph()
+        G.add_nodes_from([1, 2])
+        assert not nx.is_connected(G)
+
+    def test_connected_raise(self):
+        with pytest.raises(NetworkXNotImplemented):
+            next(nx.connected_components(self.DG))
+        pytest.raises(NetworkXNotImplemented, nx.number_connected_components, self.DG)
+        pytest.raises(NetworkXNotImplemented, nx.node_connected_component, self.DG, 1)
+        pytest.raises(NetworkXNotImplemented, nx.is_connected, self.DG)
+        pytest.raises(nx.NetworkXPointlessConcept, nx.is_connected, nx.Graph())
+
+    def test_connected_mutability(self):
+        G = self.grid
+        seen = set()
+        for component in nx.connected_components(G):
+            assert len(seen & component) == 0
+            seen.update(component)
+            component.clear()

tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/isomorphism/__init__.py

@@ -0,0 +1,7 @@
+from networkx.algorithms.isomorphism.isomorph import *
+from networkx.algorithms.isomorphism.vf2userfunc import *
+from networkx.algorithms.isomorphism.matchhelpers import *
+from networkx.algorithms.isomorphism.temporalisomorphvf2 import *
+from networkx.algorithms.isomorphism.ismags import *
+from networkx.algorithms.isomorphism.tree_isomorphism import *
+from networkx.algorithms.isomorphism.vf2pp import *