from __future__ import print_function import random from nose import SkipTest from nose.tools import assert_equal try: import numpy as np except ImportError: raise SkipTest('Numpy not available') 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_equal(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_equal(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 = in_degree_sequence = w = sequence = aseq = bseq = \ [3, 2, 1, 3, 2, 1, 3, 2, 1, 2, 1, 2, 1] # 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, .5), (10, 10, .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 = [.2, .4, .5, .7, .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.algorithms.community.LFR_benchmark_graph t(LFR, 25, 3, 1.5, 0.1, average_degree=3, min_community=10, seed=seed, max_community=20) # 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 # 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_equal(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_equal(py_rv, after_py_rv) # print("\nDone testing seed:", seed) # test_rng_interface()