two version of R2R are hereHEADmaster

1 files changed, 478 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/networkx/generators/tests/test_random_graphs.py b/.venv/lib/python3.12/site-packages/networkx/generators/tests/test_random_graphs.py
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+"""Unit tests for the :mod:`networkx.generators.random_graphs` module."""
+
+import pytest
+
+import networkx as nx
+
+_gnp_generators = [
+    nx.gnp_random_graph,
+    nx.fast_gnp_random_graph,
+    nx.binomial_graph,
+    nx.erdos_renyi_graph,
+]
+
+
+@pytest.mark.parametrize("generator", _gnp_generators)
+@pytest.mark.parametrize("directed", (True, False))
+def test_gnp_generators_negative_edge_probability(generator, directed):
+    """If the edge probability `p` is <=0, the resulting graph should have no edges."""
+    G = generator(10, -1.1, directed=directed)
+    assert len(G) == 10
+    assert G.number_of_edges() == 0
+    assert G.is_directed() == directed
+
+
+@pytest.mark.parametrize("generator", _gnp_generators)
+@pytest.mark.parametrize(
+    ("directed", "expected_num_edges"),
+    [(False, 45), (True, 90)],
+)
+def test_gnp_generators_greater_than_1_edge_probability(
+    generator, directed, expected_num_edges
+):
+    """If the edge probability `p` is >=1, the resulting graph should be complete."""
+    G = generator(10, 1.1, directed=directed)
+    assert len(G) == 10
+    assert G.number_of_edges() == expected_num_edges
+    assert G.is_directed() == directed
+
+
+@pytest.mark.parametrize("generator", _gnp_generators)
+@pytest.mark.parametrize("directed", (True, False))
+def test_gnp_generators_basic(generator, directed):
+    """If the edge probability `p` is >0 and <1, test only the basic properties."""
+    G = generator(10, 0.1, directed=directed)
+    assert len(G) == 10
+    assert G.is_directed() == directed
+
+
+@pytest.mark.parametrize("generator", _gnp_generators)
+def test_gnp_generators_for_p_close_to_1(generator):
+    """If the edge probability `p` is close to 1, the resulting graph should have all edges."""
+    runs = 100
+    edges = sum(
+        generator(10, 0.99999, directed=True).number_of_edges() for _ in range(runs)
+    )
+    assert abs(edges / float(runs) - 90) <= runs * 2.0 / 100
+
+
+@pytest.mark.parametrize("generator", _gnp_generators)
+@pytest.mark.parametrize("p", (0.2, 0.8))
+@pytest.mark.parametrize("directed", (True, False))
+def test_gnp_generators_edge_probability(generator, p, directed):
+    """Test that gnp generators generate edges according to the their probability `p`."""
+    runs = 5000
+    n = 5
+    edge_counts = [[0] * n for _ in range(n)]
+    for i in range(runs):
+        G = generator(n, p, directed=directed)
+        for v, w in G.edges:
+            edge_counts[v][w] += 1
+            if not directed:
+                edge_counts[w][v] += 1
+    for v in range(n):
+        for w in range(n):
+            if v == w:
+                # There should be no loops
+                assert edge_counts[v][w] == 0
+            else:
+                # Each edge should have been generated with probability close to p
+                assert abs(edge_counts[v][w] / float(runs) - p) <= 0.03
+
+
+@pytest.mark.parametrize(
+    "generator", [nx.gnp_random_graph, nx.binomial_graph, nx.erdos_renyi_graph]
+)
+@pytest.mark.parametrize(
+    ("seed", "directed", "expected_num_edges"),
+    [(42, False, 1219), (42, True, 2454), (314, False, 1247), (314, True, 2476)],
+)
+def test_gnp_random_graph_aliases(generator, seed, directed, expected_num_edges):
+    """Test that aliases give the same result with the same seed."""
+    G = generator(100, 0.25, seed=seed, directed=directed)
+    assert len(G) == 100
+    assert G.number_of_edges() == expected_num_edges
+    assert G.is_directed() == directed
+
+
+class TestGeneratorsRandom:
+    def test_random_graph(self):
+        seed = 42
+        G = nx.gnm_random_graph(100, 20, seed)
+        G = nx.gnm_random_graph(100, 20, seed, directed=True)
+        G = nx.dense_gnm_random_graph(100, 20, seed)
+
+        G = nx.barabasi_albert_graph(100, 1, seed)
+        G = nx.barabasi_albert_graph(100, 3, seed)
+        assert G.number_of_edges() == (97 * 3)
+
+        G = nx.barabasi_albert_graph(100, 3, seed, nx.complete_graph(5))
+        assert G.number_of_edges() == (10 + 95 * 3)
+
+        G = nx.extended_barabasi_albert_graph(100, 1, 0, 0, seed)
+        assert G.number_of_edges() == 99
+        G = nx.extended_barabasi_albert_graph(100, 3, 0, 0, seed)
+        assert G.number_of_edges() == 97 * 3
+        G = nx.extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
+        assert G.number_of_edges() == 99
+        G = nx.extended_barabasi_albert_graph(100, 2, 0.5, 0, seed)
+        assert G.number_of_edges() > 100 * 3
+        assert G.number_of_edges() < 100 * 4
+
+        G = nx.extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed)
+        assert G.number_of_edges() > 100 * 2
+        assert G.number_of_edges() < 100 * 4
+
+        G = nx.powerlaw_cluster_graph(100, 1, 1.0, seed)
+        G = nx.powerlaw_cluster_graph(100, 3, 0.0, seed)
+        assert G.number_of_edges() == (97 * 3)
+
+        G = nx.random_regular_graph(10, 20, seed)
+
+        pytest.raises(nx.NetworkXError, nx.random_regular_graph, 3, 21)
+        pytest.raises(nx.NetworkXError, nx.random_regular_graph, 33, 21)
+
+        constructor = [(10, 20, 0.8), (20, 40, 0.8)]
+        G = nx.random_shell_graph(constructor, seed)
+
+        def is_caterpillar(g):
+            """
+            A tree is a caterpillar iff all nodes of degree >=3 are surrounded
+            by at most two nodes of degree two or greater.
+            ref: http://mathworld.wolfram.com/CaterpillarGraph.html
+            """
+            deg_over_3 = [n for n in g if g.degree(n) >= 3]
+            for n in deg_over_3:
+                nbh_deg_over_2 = [nbh for nbh in g.neighbors(n) if g.degree(nbh) >= 2]
+                if not len(nbh_deg_over_2) <= 2:
+                    return False
+            return True
+
+        def is_lobster(g):
+            """
+            A tree is a lobster if it has the property that the removal of leaf
+            nodes leaves a caterpillar graph (Gallian 2007)
+            ref: http://mathworld.wolfram.com/LobsterGraph.html
+            """
+            non_leafs = [n for n in g if g.degree(n) > 1]
+            return is_caterpillar(g.subgraph(non_leafs))
+
+        G = nx.random_lobster(10, 0.1, 0.5, seed)
+        assert max(G.degree(n) for n in G.nodes()) > 3
+        assert is_lobster(G)
+        pytest.raises(nx.NetworkXError, nx.random_lobster, 10, 0.1, 1, seed)
+        pytest.raises(nx.NetworkXError, nx.random_lobster, 10, 1, 1, seed)
+        pytest.raises(nx.NetworkXError, nx.random_lobster, 10, 1, 0.5, seed)
+
+        # docstring says this should be a caterpillar
+        G = nx.random_lobster(10, 0.1, 0.0, seed)
+        assert is_caterpillar(G)
+
+        # difficult to find seed that requires few tries
+        seq = nx.random_powerlaw_tree_sequence(10, 3, seed=14, tries=1)
+        G = nx.random_powerlaw_tree(10, 3, seed=14, tries=1)
+
+    def test_dual_barabasi_albert(self, m1=1, m2=4, p=0.5):
+        """
+        Tests that the dual BA random graph generated behaves consistently.
+
+        Tests the exceptions are raised as expected.
+
+        The graphs generation are repeated several times to prevent lucky shots
+
+        """
+        seeds = [42, 314, 2718]
+        initial_graph = nx.complete_graph(10)
+
+        for seed in seeds:
+            # This should be BA with m = m1
+            BA1 = nx.barabasi_albert_graph(100, m1, seed)
+            DBA1 = nx.dual_barabasi_albert_graph(100, m1, m2, 1, seed)
+            assert BA1.edges() == DBA1.edges()
+
+            # This should be BA with m = m2
+            BA2 = nx.barabasi_albert_graph(100, m2, seed)
+            DBA2 = nx.dual_barabasi_albert_graph(100, m1, m2, 0, seed)
+            assert BA2.edges() == DBA2.edges()
+
+            BA3 = nx.barabasi_albert_graph(100, m1, seed)
+            DBA3 = nx.dual_barabasi_albert_graph(100, m1, m1, p, seed)
+            # We can't compare edges here since randomness is "consumed" when drawing
+            # between m1 and m2
+            assert BA3.size() == DBA3.size()
+
+            DBA = nx.dual_barabasi_albert_graph(100, m1, m2, p, seed, initial_graph)
+            BA1 = nx.barabasi_albert_graph(100, m1, seed, initial_graph)
+            BA2 = nx.barabasi_albert_graph(100, m2, seed, initial_graph)
+            assert (
+                min(BA1.size(), BA2.size()) <= DBA.size() <= max(BA1.size(), BA2.size())
+            )
+
+        # Testing exceptions
+        dbag = nx.dual_barabasi_albert_graph
+        pytest.raises(nx.NetworkXError, dbag, m1, m1, m2, 0)
+        pytest.raises(nx.NetworkXError, dbag, m2, m1, m2, 0)
+        pytest.raises(nx.NetworkXError, dbag, 100, m1, m2, -0.5)
+        pytest.raises(nx.NetworkXError, dbag, 100, m1, m2, 1.5)
+        initial = nx.complete_graph(max(m1, m2) - 1)
+        pytest.raises(nx.NetworkXError, dbag, 100, m1, m2, p, initial_graph=initial)
+
+    def test_extended_barabasi_albert(self, m=2):
+        """
+        Tests that the extended BA random graph generated behaves consistently.
+
+        Tests the exceptions are raised as expected.
+
+        The graphs generation are repeated several times to prevent lucky-shots
+
+        """
+        seeds = [42, 314, 2718]
+
+        for seed in seeds:
+            BA_model = nx.barabasi_albert_graph(100, m, seed)
+            BA_model_edges = BA_model.number_of_edges()
+
+            # This behaves just like BA, the number of edges must be the same
+            G1 = nx.extended_barabasi_albert_graph(100, m, 0, 0, seed)
+            assert G1.size() == BA_model_edges
+
+            # More than twice more edges should have been added
+            G1 = nx.extended_barabasi_albert_graph(100, m, 0.8, 0, seed)
+            assert G1.size() > BA_model_edges * 2
+
+            # Only edge rewiring, so the number of edges less than original
+            G2 = nx.extended_barabasi_albert_graph(100, m, 0, 0.8, seed)
+            assert G2.size() == BA_model_edges
+
+            # Mixed scenario: less edges than G1 and more edges than G2
+            G3 = nx.extended_barabasi_albert_graph(100, m, 0.3, 0.3, seed)
+            assert G3.size() > G2.size()
+            assert G3.size() < G1.size()
+
+        # Testing exceptions
+        ebag = nx.extended_barabasi_albert_graph
+        pytest.raises(nx.NetworkXError, ebag, m, m, 0, 0)
+        pytest.raises(nx.NetworkXError, ebag, 1, 0.5, 0, 0)
+        pytest.raises(nx.NetworkXError, ebag, 100, 2, 0.5, 0.5)
+
+    def test_random_zero_regular_graph(self):
+        """Tests that a 0-regular graph has the correct number of nodes and
+        edges.
+
+        """
+        seed = 42
+        G = nx.random_regular_graph(0, 10, seed)
+        assert len(G) == 10
+        assert G.number_of_edges() == 0
+
+    def test_gnm(self):
+        G = nx.gnm_random_graph(10, 3)
+        assert len(G) == 10
+        assert G.number_of_edges() == 3
+
+        G = nx.gnm_random_graph(10, 3, seed=42)
+        assert len(G) == 10
+        assert G.number_of_edges() == 3
+
+        G = nx.gnm_random_graph(10, 100)
+        assert len(G) == 10
+        assert G.number_of_edges() == 45
+
+        G = nx.gnm_random_graph(10, 100, directed=True)
+        assert len(G) == 10
+        assert G.number_of_edges() == 90
+
+        G = nx.gnm_random_graph(10, -1.1)
+        assert len(G) == 10
+        assert G.number_of_edges() == 0
+
+    def test_watts_strogatz_big_k(self):
+        # Test to make sure than n <= k
+        pytest.raises(nx.NetworkXError, nx.watts_strogatz_graph, 10, 11, 0.25)
+        pytest.raises(nx.NetworkXError, nx.newman_watts_strogatz_graph, 10, 11, 0.25)
+
+        # could create an infinite loop, now doesn't
+        # infinite loop used to occur when a node has degree n-1 and needs to rewire
+        nx.watts_strogatz_graph(10, 9, 0.25, seed=0)
+        nx.newman_watts_strogatz_graph(10, 9, 0.5, seed=0)
+
+        # Test k==n scenario
+        nx.watts_strogatz_graph(10, 10, 0.25, seed=0)
+        nx.newman_watts_strogatz_graph(10, 10, 0.25, seed=0)
+
+    def test_random_kernel_graph(self):
+        def integral(u, w, z):
+            return c * (z - w)
+
+        def root(u, w, r):
+            return r / c + w
+
+        c = 1
+        graph = nx.random_kernel_graph(1000, integral, root)
+        graph = nx.random_kernel_graph(1000, integral, root, seed=42)
+        assert len(graph) == 1000
+
+
+@pytest.mark.parametrize(
+    ("k", "expected_num_nodes", "expected_num_edges"),
+    [
+        (2, 10, 10),
+        (4, 10, 20),
+    ],
+)
+def test_watts_strogatz(k, expected_num_nodes, expected_num_edges):
+    G = nx.watts_strogatz_graph(10, k, 0.25, seed=42)
+    assert len(G) == expected_num_nodes
+    assert G.number_of_edges() == expected_num_edges
+
+
+def test_newman_watts_strogatz_zero_probability():
+    G = nx.newman_watts_strogatz_graph(10, 2, 0.0, seed=42)
+    assert len(G) == 10
+    assert G.number_of_edges() == 10
+
+
+def test_newman_watts_strogatz_nonzero_probability():
+    G = nx.newman_watts_strogatz_graph(10, 4, 0.25, seed=42)
+    assert len(G) == 10
+    assert G.number_of_edges() >= 20
+
+
+def test_connected_watts_strogatz():
+    G = nx.connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=42)
+    assert len(G) == 10
+    assert G.number_of_edges() == 10
+
+
+def test_connected_watts_strogatz_zero_tries():
+    with pytest.raises(nx.NetworkXError, match="Maximum number of tries exceeded"):
+        nx.connected_watts_strogatz_graph(10, 2, 0.1, tries=0)
+
+
+@pytest.mark.parametrize(
+    "generator, kwargs",
+    [
+        (nx.fast_gnp_random_graph, {"n": 20, "p": 0.2, "directed": False}),
+        (nx.fast_gnp_random_graph, {"n": 20, "p": 0.2, "directed": True}),
+        (nx.gnp_random_graph, {"n": 20, "p": 0.2, "directed": False}),
+        (nx.gnp_random_graph, {"n": 20, "p": 0.2, "directed": True}),
+        (nx.dense_gnm_random_graph, {"n": 30, "m": 4}),
+        (nx.gnm_random_graph, {"n": 30, "m": 4, "directed": False}),
+        (nx.gnm_random_graph, {"n": 30, "m": 4, "directed": True}),
+        (nx.newman_watts_strogatz_graph, {"n": 50, "k": 5, "p": 0.1}),
+        (nx.watts_strogatz_graph, {"n": 50, "k": 5, "p": 0.1}),
+        (nx.connected_watts_strogatz_graph, {"n": 50, "k": 5, "p": 0.1}),
+        (nx.random_regular_graph, {"d": 5, "n": 20}),
+        (nx.barabasi_albert_graph, {"n": 40, "m": 3}),
+        (nx.dual_barabasi_albert_graph, {"n": 40, "m1": 3, "m2": 2, "p": 0.1}),
+        (nx.extended_barabasi_albert_graph, {"n": 40, "m": 3, "p": 0.1, "q": 0.2}),
+        (nx.powerlaw_cluster_graph, {"n": 40, "m": 3, "p": 0.1}),
+        (nx.random_lobster, {"n": 40, "p1": 0.1, "p2": 0.2}),
+        (nx.random_shell_graph, {"constructor": [(10, 20, 0.8), (20, 40, 0.8)]}),
+        (nx.random_powerlaw_tree, {"n": 10, "seed": 14, "tries": 1}),
+        (
+            nx.random_kernel_graph,
+            {
+                "n": 10,
+                "kernel_integral": lambda u, w, z: z - w,
+                "kernel_root": lambda u, w, r: r + w,
+            },
+        ),
+    ],
+)
+@pytest.mark.parametrize("create_using_instance", [False, True])
+def test_create_using(generator, kwargs, create_using_instance):
+    class DummyGraph(nx.Graph):
+        pass
+
+    class DummyDiGraph(nx.DiGraph):
+        pass
+
+    create_using_type = DummyDiGraph if kwargs.get("directed") else DummyGraph
+    create_using = create_using_type() if create_using_instance else create_using_type
+    graph = generator(**kwargs, create_using=create_using)
+    assert isinstance(graph, create_using_type)
+
+
+@pytest.mark.parametrize("directed", [True, False])
+@pytest.mark.parametrize("fn", (nx.fast_gnp_random_graph, nx.gnp_random_graph))
+def test_gnp_fns_disallow_multigraph(fn, directed):
+    with pytest.raises(nx.NetworkXError, match="must not be a multi-graph"):
+        fn(20, 0.2, create_using=nx.MultiGraph)
+
+
+@pytest.mark.parametrize("fn", (nx.gnm_random_graph, nx.dense_gnm_random_graph))
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_gnm_fns_disallow_directed_and_multigraph(fn, graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        fn(10, 20, create_using=graphtype)
+
+
+@pytest.mark.parametrize(
+    "fn",
+    (
+        nx.newman_watts_strogatz_graph,
+        nx.watts_strogatz_graph,
+        nx.connected_watts_strogatz_graph,
+    ),
+)
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_watts_strogatz_disallow_directed_and_multigraph(fn, graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        fn(10, 2, 0.2, create_using=graphtype)
+
+
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_random_regular_graph_disallow_directed_and_multigraph(graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        nx.random_regular_graph(2, 10, create_using=graphtype)
+
+
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_barabasi_albert_disallow_directed_and_multigraph(graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        nx.barabasi_albert_graph(10, 3, create_using=graphtype)
+
+
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_dual_barabasi_albert_disallow_directed_and_multigraph(graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        nx.dual_barabasi_albert_graph(10, 2, 1, 0.4, create_using=graphtype)
+
+
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_extended_barabasi_albert_disallow_directed_and_multigraph(graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        nx.extended_barabasi_albert_graph(10, 2, 0.2, 0.3, create_using=graphtype)
+
+
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_powerlaw_cluster_disallow_directed_and_multigraph(graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        nx.powerlaw_cluster_graph(10, 5, 0.2, create_using=graphtype)
+
+
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_random_lobster_disallow_directed_and_multigraph(graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        nx.random_lobster(10, 0.1, 0.1, create_using=graphtype)
+
+
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_random_shell_disallow_directed_and_multigraph(graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        nx.random_shell_graph([(10, 20, 2), (10, 20, 5)], create_using=graphtype)
+
+
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_random_powerlaw_tree_disallow_directed_and_multigraph(graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        nx.random_powerlaw_tree(10, create_using=graphtype)
+
+
+@pytest.mark.parametrize("graphtype", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+def test_random_kernel_disallow_directed_and_multigraph(graphtype):
+    with pytest.raises(nx.NetworkXError, match="must not be"):
+        nx.random_kernel_graph(
+            10, lambda y, a, b: a + b, lambda u, w, r: r + w, create_using=graphtype
+        )