Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/tests/test_all_random_functions.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/tests/test_all_random_functions.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,233 @@ +import pytest + +np = pytest.importorskip("numpy") +import random + +import networkx as nx +from networkx.algorithms import approximation as approx +from networkx.algorithms import threshold + +progress = 0 + +# store the random numbers after setting a global seed +np.random.seed(42) +np_rv = np.random.rand() +random.seed(42) +py_rv = random.random() + + +def t(f, *args, **kwds): + """call one function and check if global RNG changed""" + global progress + progress += 1 + print(progress, ",", end="") + + f(*args, **kwds) + + after_np_rv = np.random.rand() + # if np_rv != after_np_rv: + # print(np_rv, after_np_rv, "don't match np!") + assert np_rv == after_np_rv + np.random.seed(42) + + after_py_rv = random.random() + # if py_rv != after_py_rv: + # print(py_rv, after_py_rv, "don't match py!") + assert py_rv == after_py_rv + random.seed(42) + + +def run_all_random_functions(seed): + n = 20 + m = 10 + k = l = 2 + s = v = 10 + p = q = p1 = p2 = p_in = p_out = 0.4 + alpha = radius = theta = 0.75 + sizes = (20, 20, 10) + colors = [1, 2, 3] + G = nx.barbell_graph(12, 20) + deg_sequence = [3, 2, 1, 3, 2, 1, 3, 2, 1, 2, 1, 2, 1] + in_degree_sequence = w = sequence = aseq = bseq = deg_sequence + + # print("starting...") + t(nx.maximal_independent_set, G, seed=seed) + t(nx.rich_club_coefficient, G, seed=seed, normalized=False) + t(nx.random_reference, G, seed=seed) + t(nx.lattice_reference, G, seed=seed) + t(nx.sigma, G, 1, 2, seed=seed) + t(nx.omega, G, 1, 2, seed=seed) + # print("out of smallworld.py") + t(nx.double_edge_swap, G, seed=seed) + # print("starting connected_double_edge_swap") + t(nx.connected_double_edge_swap, nx.complete_graph(9), seed=seed) + # print("ending connected_double_edge_swap") + t(nx.random_layout, G, seed=seed) + t(nx.fruchterman_reingold_layout, G, seed=seed) + t(nx.algebraic_connectivity, G, seed=seed) + t(nx.fiedler_vector, G, seed=seed) + t(nx.spectral_ordering, G, seed=seed) + # print('starting average_clustering') + t(approx.average_clustering, G, seed=seed) + t(nx.betweenness_centrality, G, seed=seed) + t(nx.edge_betweenness_centrality, G, seed=seed) + t(nx.edge_betweenness, G, seed=seed) + t(nx.approximate_current_flow_betweenness_centrality, G, seed=seed) + # print("kernighan") + t(nx.algorithms.community.kernighan_lin_bisection, G, seed=seed) + # nx.algorithms.community.asyn_lpa_communities(G, seed=seed) + t(nx.algorithms.tree.greedy_branching, G, seed=seed) + t(nx.algorithms.tree.Edmonds, G, seed=seed) + # print('done with graph argument functions') + + t(nx.spectral_graph_forge, G, alpha, seed=seed) + t(nx.algorithms.community.asyn_fluidc, G, k, max_iter=1, seed=seed) + t( + nx.algorithms.connectivity.edge_augmentation.greedy_k_edge_augmentation, + G, + k, + seed=seed, + ) + t(nx.algorithms.coloring.strategy_random_sequential, G, colors, seed=seed) + + cs = ["d", "i", "i", "d", "d", "i"] + t(threshold.swap_d, cs, seed=seed) + t(nx.configuration_model, deg_sequence, seed=seed) + t( + nx.directed_configuration_model, + in_degree_sequence, + in_degree_sequence, + seed=seed, + ) + t(nx.expected_degree_graph, w, seed=seed) + t(nx.random_degree_sequence_graph, sequence, seed=seed) + joint_degrees = { + 1: {4: 1}, + 2: {2: 2, 3: 2, 4: 2}, + 3: {2: 2, 4: 1}, + 4: {1: 1, 2: 2, 3: 1}, + } + t(nx.joint_degree_graph, joint_degrees, seed=seed) + joint_degree_sequence = [ + (1, 0), + (1, 0), + (1, 0), + (2, 0), + (1, 0), + (2, 1), + (0, 1), + (0, 1), + ] + t(nx.random_clustered_graph, joint_degree_sequence, seed=seed) + constructor = [(3, 3, 0.5), (10, 10, 0.7)] + t(nx.random_shell_graph, constructor, seed=seed) + mapping = {1: 0.4, 2: 0.3, 3: 0.3} + t(nx.utils.random_weighted_sample, mapping, k, seed=seed) + t(nx.utils.weighted_choice, mapping, seed=seed) + t(nx.algorithms.bipartite.configuration_model, aseq, bseq, seed=seed) + t(nx.algorithms.bipartite.preferential_attachment_graph, aseq, p, seed=seed) + + def kernel_integral(u, w, z): + return z - w + + t(nx.random_kernel_graph, n, kernel_integral, seed=seed) + + sizes = [75, 75, 300] + probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]] + t(nx.stochastic_block_model, sizes, probs, seed=seed) + t(nx.random_partition_graph, sizes, p_in, p_out, seed=seed) + + # print("starting generator functions") + t(threshold.random_threshold_sequence, n, p, seed=seed) + t(nx.tournament.random_tournament, n, seed=seed) + t(nx.relaxed_caveman_graph, l, k, p, seed=seed) + t(nx.planted_partition_graph, l, k, p_in, p_out, seed=seed) + t(nx.gaussian_random_partition_graph, n, s, v, p_in, p_out, seed=seed) + t(nx.gn_graph, n, seed=seed) + t(nx.gnr_graph, n, p, seed=seed) + t(nx.gnc_graph, n, seed=seed) + t(nx.scale_free_graph, n, seed=seed) + t(nx.directed.random_uniform_k_out_graph, n, k, seed=seed) + t(nx.random_k_out_graph, n, k, alpha, seed=seed) + N = 1000 + t(nx.partial_duplication_graph, N, n, p, q, seed=seed) + t(nx.duplication_divergence_graph, n, p, seed=seed) + t(nx.random_geometric_graph, n, radius, seed=seed) + t(nx.soft_random_geometric_graph, n, radius, seed=seed) + t(nx.geographical_threshold_graph, n, theta, seed=seed) + t(nx.waxman_graph, n, seed=seed) + t(nx.navigable_small_world_graph, n, seed=seed) + t(nx.thresholded_random_geometric_graph, n, radius, theta, seed=seed) + t(nx.uniform_random_intersection_graph, n, m, p, seed=seed) + t(nx.k_random_intersection_graph, n, m, k, seed=seed) + + t(nx.general_random_intersection_graph, n, 2, [0.1, 0.5], seed=seed) + t(nx.fast_gnp_random_graph, n, p, seed=seed) + t(nx.gnp_random_graph, n, p, seed=seed) + t(nx.dense_gnm_random_graph, n, m, seed=seed) + t(nx.gnm_random_graph, n, m, seed=seed) + t(nx.newman_watts_strogatz_graph, n, k, p, seed=seed) + t(nx.watts_strogatz_graph, n, k, p, seed=seed) + t(nx.connected_watts_strogatz_graph, n, k, p, seed=seed) + t(nx.random_regular_graph, 3, n, seed=seed) + t(nx.barabasi_albert_graph, n, m, seed=seed) + t(nx.extended_barabasi_albert_graph, n, m, p, q, seed=seed) + t(nx.powerlaw_cluster_graph, n, m, p, seed=seed) + t(nx.random_lobster, n, p1, p2, seed=seed) + t(nx.random_powerlaw_tree, n, seed=seed, tries=5000) + t(nx.random_powerlaw_tree_sequence, 10, seed=seed, tries=5000) + t(nx.random_tree, n, seed=seed) + t(nx.utils.powerlaw_sequence, n, seed=seed) + t(nx.utils.zipf_rv, 2.3, seed=seed) + cdist = [0.2, 0.4, 0.5, 0.7, 0.9, 1.0] + t(nx.utils.discrete_sequence, n, cdistribution=cdist, seed=seed) + t(nx.algorithms.bipartite.random_graph, n, m, p, seed=seed) + t(nx.algorithms.bipartite.gnmk_random_graph, n, m, k, seed=seed) + LFR = nx.generators.LFR_benchmark_graph + t( + LFR, + 25, + 3, + 1.5, + 0.1, + average_degree=3, + min_community=10, + seed=seed, + max_community=20, + ) + t(nx.random_internet_as_graph, n, seed=seed) + # print("done") + + +# choose to test an integer seed, or whether a single RNG can be everywhere +# np_rng = np.random.RandomState(14) +# seed = np_rng +# seed = 14 + + +@pytest.mark.slow +# print("NetworkX Version:", nx.__version__) +def test_rng_interface(): + global progress + + # try different kinds of seeds + for seed in [14, np.random.RandomState(14)]: + np.random.seed(42) + random.seed(42) + run_all_random_functions(seed) + progress = 0 + + # check that both global RNGs are unaffected + after_np_rv = np.random.rand() + # if np_rv != after_np_rv: + # print(np_rv, after_np_rv, "don't match np!") + assert np_rv == after_np_rv + after_py_rv = random.random() + # if py_rv != after_py_rv: + # print(py_rv, after_py_rv, "don't match py!") + assert py_rv == after_py_rv + + +# print("\nDone testing seed:", seed) + +# test_rng_interface()