changes for runnning functions

4 files changed, 20 insertions, 7 deletions

diff --git a/src/algorithms.py b/src/algorithms.py
index 9f6d3ea..fb0a37d 100644
--- a/src/algorithms.py
+++ b/src/algorithms.py
@@ -54,7 +54,7 @@ def recovery_passed_function(G, cascades, floor_cstt, passed_function,
     return G_hat
 
 
-def correctness_measure(G, G_hat, print_values=False):
+def correctness_measure(G, G_hat, print_values=True):
     """
     Measures correctness of estimated graph G_hat to ground truth G
     """
diff --git a/src/cascade_creation.py b/src/cascade_creation.py
index c297f76..df0384e 100644
--- a/src/cascade_creation.py
+++ b/src/cascade_creation.py
@@ -25,8 +25,8 @@ class InfluenceGraph(nx.DiGraph):
         p : proba of rewiring edge
         """
         G = nx.connected_watts_strogatz_graph(n, k, p)
-        G = nx.convert_to_directed(G)
         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):
         """
@@ -153,7 +153,6 @@ def add_edges_from_proba_vector(G, p_node, node, floor_cstt):
     Also updates G's mat matrix
     """
     floor_parent = np.nonzero(p_node*(p_node > floor_cstt))
-    print(floor_parent)
     for parent in floor_parent[0]:
         G.add_edge(parent, node)
     #TODO: update G's mat matrix
diff --git a/src/convex_optimization.py b/src/convex_optimization.py
index f283d6b..f4c997a 100644
--- a/src/convex_optimization.py
+++ b/src/convex_optimization.py
@@ -80,7 +80,7 @@ def type_lasso(lbda):
 
     return f_x, f_xz
 
-@timeout.timeout(8)
+@timeout.timeout(30)
 def diff_and_opt(M_val, w_val, f_x, f_xz):
 
     if M_val.dtype == bool:
@@ -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(-.001, (n,1))
+            return 0, cvxopt.matrix(-.0001, (n,1))
         elif z is None:
             y, y_diff = f_x(x, M_val, w_val)
             return cvxopt.matrix(float(y), (1, 1)),\
@@ -108,7 +108,7 @@ def diff_and_opt(M_val, w_val, f_x, f_xz):
     #cvxopt.solvers.options['feastol'] = 2e-5
     #cvxopt.solvers.options['abstol'] = 2e-5
     #cvxopt.solvers.options['maxiters'] = 100
-    cvxopt.solvers.options['show_progress'] = False
+    cvxopt.solvers.options['show_progress'] = True
     try:
         theta = cvxopt.solvers.cp(F, G, h)['x']
     except ArithmeticError:
diff --git a/src/make_plots.py b/src/make_plots.py
index bfe78f8..18864d3 100644
--- a/src/make_plots.py
+++ b/src/make_plots.py
@@ -27,6 +27,19 @@ def compare_greedy_and_lagrange_cs284r():
     algorithms.correctness_measure(G, G_hat, print_values=True)
 
 
+def watts_strogatz(n_cascades, lbda, passed_function):
+    """
+    Test running time on different algorithms
+    """
+    G = cascade_creation.InfluenceGraph(max_proba=1, min_proba=.2)
+    G.import_from_file("../datasets/watts_strogatz_500_80_point3.txt")
+    A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=n_cascades)
+
+    G_hat = algorithms.recovery_passed_function(G, A,
+            passed_function=passed_function,
+            floor_cstt=.1, lbda=lbda, n_cascades=1000)
+
+
 def test():
     """
     unit test
@@ -41,4 +54,5 @@ def test():
 
 
 if __name__=="__main__":
-    test()
+    watts_strogatz(n_cascades=1000, lbda=.001, passed_function=
+        convex_optimization.sparse_recovery)