From a819813ca6c310d84a52de51e7bc49ea8dd8a726 Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Wed, 4 Feb 2015 12:35:03 -0500
Subject: adding datasets, sorry T-bone

---
 datasets/barabasi_albert_300_30.txt    | 16200 +++++++++++++++++++++++++++++++
 datasets/kronecker_graph_256_cross.txt | 10000 +++++++++++++++++++
 paper/figures/watts_strogatz.pdf       |   Bin 13549 -> 13719 bytes
 src/cascade_creation.py                |     9 +
 src/convex_optimization.py             |     2 +-
 src/make_plots.py                      |    43 +-
 6 files changed, 26248 insertions(+), 6 deletions(-)
 create mode 100644 datasets/barabasi_albert_300_30.txt
 create mode 100644 datasets/kronecker_graph_256_cross.txt

diff --git a/datasets/barabasi_albert_300_30.txt b/datasets/barabasi_albert_300_30.txt
new file mode 100644
index 0000000..684cf85
--- /dev/null
+++ b/datasets/barabasi_albert_300_30.txt
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diff --git a/datasets/kronecker_graph_256_cross.txt b/datasets/kronecker_graph_256_cross.txt
new file mode 100644
index 0000000..f819a31
--- /dev/null
+++ b/datasets/kronecker_graph_256_cross.txt
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diff --git a/paper/figures/watts_strogatz.pdf b/paper/figures/watts_strogatz.pdf
index f743d87..cdc2bfa 100644
Binary files a/paper/figures/watts_strogatz.pdf and b/paper/figures/watts_strogatz.pdf differ
diff --git a/src/cascade_creation.py b/src/cascade_creation.py
index a90939b..1c365b9 100644
--- a/src/cascade_creation.py
+++ b/src/cascade_creation.py
@@ -28,6 +28,15 @@ 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 barabasi_albert(self, n, m):
+        """
+        n : number of nodes
+        m : number of edges to attach to new node
+        """
+        G = nx.barabasi_albert_graph(n, m)
+        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
diff --git a/src/convex_optimization.py b/src/convex_optimization.py
index 3111e50..0ff4521 100644
--- a/src/convex_optimization.py
+++ b/src/convex_optimization.py
@@ -90,7 +90,7 @@ def diff_and_opt(M_val, w_val, f_x, f_xz):
 
     def F(x=None, z=None):
         if x is None:
-            return 0, cvxopt.matrix(-.00001, (n,1))
+            return 0, cvxopt.matrix(-.1, (n,1))
         elif z is None:
             y, y_diff = f_x(x, M_val, w_val)
             return cvxopt.matrix(float(y), (1, 1)),\
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)
\ No newline at end of file
-- 
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