diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/cascade_creation.py | 11 | ||||
| -rw-r--r-- | src/make_plots.py | 31 |
2 files changed, 31 insertions, 11 deletions
diff --git a/src/cascade_creation.py b/src/cascade_creation.py index df0384e..a90939b 100644 --- a/src/cascade_creation.py +++ b/src/cascade_creation.py @@ -28,6 +28,17 @@ class InfluenceGraph(nx.DiGraph): self.add_edges_from(G.edges()) self.add_edges_from([(node_2, node_1) for node_1, node_2 in G.edges()]) + def powerlaw_cluster(self, n, m, p, seed=139): + """ + n : number of nodes + m : number of random edges to add for each node + p : proba of adding triangle for each random edge + seed : random number generator + """ + G = nx.powerlaw_cluster_graph(n, m, p, seed) + self.add_edges_from(G.edges()) + self.add_edges_from([(node_2, node_1) for node_1, node_2 in G.edges()]) + def import_from_file(self, file_name): """ Takes a file from the Stanford collection of networks diff --git a/src/make_plots.py b/src/make_plots.py index 556d27a..980b613 100644 --- a/src/make_plots.py +++ b/src/make_plots.py @@ -27,12 +27,12 @@ def compare_greedy_and_lagrange_cs284r(): algorithms.correctness_measure(G, G_hat, print_values=True) -def watts_strogatz(n_cascades, lbda, passed_function): +def compute_graph(graph_name, n_cascades, lbda, passed_function): """ Test running time on different algorithms """ G = cascade_creation.InfluenceGraph(max_proba=.7, min_proba=.2) - G.import_from_file("../datasets/watts_strogatz_300_30_point3.txt") + G.import_from_file(graph_name) A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=n_cascades) if passed_function==algorithms.greedy_prediction: @@ -49,12 +49,13 @@ def plot_graph(figure_name): plot information in a pretty way """ plt.clf() - x = [np.log(50), np.log(100), np.log(500), np.log(1000), np.log(2000), np.log(5000)] - greedy = [.09, .15, .4, .63, .82, .92] - lasso = [.07, .30, .46, .65, 0, 0] - max_likel = [.21, .29, .67, .8, .87, .9] + x = [50, 100, 500, 1000, 2000, 5000] sparse_recov = [.25, .32, .7, .82, .89, .92] - plt.axis((0, np.log(6000), 0, 1)) + max_likel = [.21, .29, .67, .8, .87, .9] + lasso = [.07, .30, .46, .65, 0, 0] + greedy = [.09, .15, .4, .63, .82, .92] + + plt.axis((0, 5500, 0, 1)) plt.xlabel("Number of Cascades") plt.ylabel("F1 score") plt.grid(color="lightgrey") @@ -67,7 +68,15 @@ def plot_graph(figure_name): if __name__=="__main__": - watts_strogatz(n_cascades=5000, lbda=.002, passed_function= - #convex_optimization.sparse_recovery) - #algorithms.greedy_prediction) - convex_optimization.type_lasso)
\ No newline at end of file + if 0: + compute_graph("../datasets/watts_strogatz_300_30_point3.txt", + n_cascades=5000, lbda=.002, passed_function= + #convex_optimization.sparse_recovery) + #algorithms.greedy_prediction) + convex_optimization.type_lasso) + if 1: + compute_graph("../datasets/watts_strogatz_300_30_point3.txt", + n_cascades=300, lbda=.002, passed_function= + convex_optimization.sparse_recovery) + #algorithms.greedy_prediction) + #convex_optimization.type_lasso)
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