some minor changes

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)
\ No newline at end of file