routine commit

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