diff options
Diffstat (limited to 'src/make_plots.py')
| -rw-r--r-- | src/make_plots.py | 40 |
1 files changed, 29 insertions, 11 deletions
diff --git a/src/make_plots.py b/src/make_plots.py index 28d89e2..10fd657 100644 --- a/src/make_plots.py +++ b/src/make_plots.py @@ -27,11 +27,14 @@ def compare_greedy_and_lagrange_cs284r(): algorithms.correctness_measure(G, G_hat, print_values=True) -def compute_graph(graph_name, n_cascades, lbda, passed_function): +def compute_graph(graph_name, n_cascades, lbda, passed_function, min_proba, + max_proba, sparse_edges=False): """ Test running time on different algorithms """ - G = cascade_creation.InfluenceGraph(max_proba=.7, min_proba=.2) + G = cascade_creation.InfluenceGraph(max_proba=max_proba, + min_proba=min_proba, + sparse_edges=True) G.import_from_file(graph_name) A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=n_cascades) @@ -51,7 +54,8 @@ def plot_watts_strogatz_graph(): plt.clf() fig = plt.figure(1) labels = [50, 100, 500, 1000, 2000, 5000] - x = [np.log(50), np.log(100), np.log(500), np.log(1000), np.log(2000), np.log(5000)] + x = [np.log(50), np.log(100), np.log(500), + np.log(1000), np.log(2000), np.log(5000)] sparse_recov = [.25, .32, .7, .82, .89, .92] max_likel = [.21, .29, .67, .8, .87, .9] lasso = [.07, .30, .46, .65, .86, .89] @@ -97,10 +101,14 @@ def plot_ROC_curve(figure_name): plt.xlabel("Recall") plt.ylabel("Precision") plt.grid(color="lightgrey") - ax.plot(recall_lasso_200, precision_lasso_200, 'ko-', color="lightseagreen", label="Lasso-200 cascades") - ax.plot(recall_sparse_200, precision_sparse_200, 'ko-', color="k", label="Our Method-200 cascades") - ax.plot(recall_lasso_50, precision_lasso_50, 'ko-', color="orange", label="Lasso-50 cascades") - ax.plot(recall_sparse_50, precision_sparse_50, 'ko-', color="cornflowerblue", label="Our Method-50 cascades") + ax.plot(recall_lasso_200, precision_lasso_200, 'ko-', + color="lightseagreen", label="Lasso-200 cascades") + ax.plot(recall_sparse_200, precision_sparse_200, 'ko-', + color="k", label="Our Method-200 cascades") + ax.plot(recall_lasso_50, precision_lasso_50, 'ko-', + color="orange", label="Lasso-50 cascades") + ax.plot(recall_sparse_50, precision_sparse_50, 'ko-', + color="cornflowerblue", label="Our Method-50 cascades") plt.legend(loc="upper right") plt.savefig("../paper/figures/"+"ROC_curve.pdf") @@ -108,19 +116,29 @@ def plot_ROC_curve(figure_name): if __name__=="__main__": if 0: compute_graph("../datasets/watts_strogatz_300_30_point3.txt", - n_cascades=100, lbda=.01, passed_function= + n_cascades=100, lbda=.01, min_proba=.2, max_proba=.7, + passed_function= #convex_optimization.sparse_recovery) #algorithms.greedy_prediction) convex_optimization.sparse_recovery) if 0: compute_graph("../datasets/powerlaw_200_30_point3.txt", - n_cascades=200, lbda=.01, passed_function= + n_cascades=200, lbda=.01, min_proba=.2, max_proba=.7, + passed_function= #convex_optimization.sparse_recovery) #algorithms.greedy_prediction) convex_optimization.type_lasso) if 0: compute_graph("../datasets/barabasi_albert_300_30.txt", - n_cascades=100, lbda=.002, passed_function= + n_cascades=100, lbda=.002, min_proba=.2, + max_proba=.7, passed_function= convex_optimization.sparse_recovery) #algorithms.greedy_prediction) - #convex_optimization.type_lasso)
\ No newline at end of file + #convex_optimization.type_lasso) + if 1: + compute_graph("../datasets/kronecker_graph_256_cross.txt", + n_cascades=1000, lbda=.001, min_proba=.2, max_proba=.7, + passed_function= + convex_optimization.sparse_recovery, + #convex_optimization.type_lasso, + sparse_edges=False)
\ No newline at end of file |