diff options
Diffstat (limited to 'src/make_plots.py')
| -rw-r--r-- | src/make_plots.py | 43 |
1 files changed, 38 insertions, 5 deletions
diff --git a/src/make_plots.py b/src/make_plots.py index aa77365..9b0fc11 100644 --- a/src/make_plots.py +++ b/src/make_plots.py @@ -44,7 +44,7 @@ def compute_graph(graph_name, n_cascades, lbda, passed_function): algorithms.correctness_measure(G, G_hat, print_values=True) -def plot_graph(figure_name): +def plot_watts_strogatz_graph(): """ plot information in a pretty way """ @@ -65,24 +65,57 @@ def plot_graph(figure_name): plt.grid(color="lightgrey") ax.plot(x, greedy, 'ko-', color="lightseagreen", label='Greedy') ax.plot(x, lasso, 'ko-', color="orange", label="Lasso") - ax.plot(x, max_likel, 'ko-', color="coral", label="MLE") + ax.plot(x, max_likel, 'ko-', color="cornflowerblue", label="MLE") ax.plot(x, sparse_recov, 'ko-', color="k", label="Our Method") plt.legend(loc="lower right") ax.set_xticks(x) ax.set_xticklabels(tuple(labels)) + plt.savefig("../paper/figures/"+"watts_strogatz.pdf") + + +def plot_ROC_curve(figure_name): + """ + plot information in a pretty way + """ + plt.clf() + fig = plt.figure(1) + #labels = [0, .00002, .002, .02, .2, .5] + x_sparse = [.57, .6, .61, .76, .9] + y_sparse = [.41, .4, .37, .16, .03] + + x_lasso = [.55, .56, .66] + y_lasso = [.5, .43, .25] + + fig, ax = plt.subplots() + + plt.axis((np.log(45), np.log(5500), 0, 1)) + plt.xlabel("Number of Cascades") + plt.ylabel("F1 score") + plt.grid(color="lightgrey") + ax.plot(x_lasso, y_lasso, 'ko-', color="orange", label="Lasso") + ax.plot(x_sparse, y_sparse, 'ko-', color="k", label="Our Method") + plt.legend(loc="lower right") + ax.set_xticks(x) + ax.set_xticklabels(tuple(labels)) plt.savefig("../paper/figures/"+figure_name) if __name__=="__main__": if 0: compute_graph("../datasets/watts_strogatz_300_30_point3.txt", - n_cascades=5000, lbda=.002, passed_function= + n_cascades=100, lbda=.01, passed_function= #convex_optimization.sparse_recovery) #algorithms.greedy_prediction) - convex_optimization.type_lasso) - if 1: + convex_optimization.sparse_recovery) + if 0: compute_graph("../datasets/powerlaw_200_30_point3.txt", n_cascades=300, lbda=.002, passed_function= convex_optimization.sparse_recovery) #algorithms.greedy_prediction) + #convex_optimization.type_lasso) + if 1: + compute_graph("../datasets/barabasi_albert_300_30.txt", + n_cascades=100, lbda=.002, passed_function= + convex_optimization.sparse_recovery) + #algorithms.greedy_prediction) #convex_optimization.type_lasso)
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