more changes

3 files changed, 16 insertions, 12 deletions

diff --git a/src/cascade_creation.py b/src/cascade_creation.py
index 93ce662..39fc531 100644
--- a/src/cascade_creation.py
+++ b/src/cascade_creation.py
@@ -1,6 +1,7 @@
 import networkx as nx
 import numpy as np
 import collections
+import timeout
 #from itertools import izip
 from sklearn.preprocessing import normalize
 
@@ -70,6 +71,7 @@ class Cascade(list):
         return candidate_infectors
 
 
+@timeout.timeout(5)
 def icc_cascade(G, p_init):
     """
     Returns boolean vectors for one cascade
@@ -87,7 +89,6 @@ def icc_cascade(G, p_init):
         active = active & susceptible
         susceptible = susceptible & np.logical_not(active)
     if not cascade:
-        print("Empty cascade, consider changing p_init or n_nodes. Retrying.")
         return icc_cascade(G, p_init)
     return cascade
 
diff --git a/src/convex_optimization.py b/src/convex_optimization.py
index f9be47d..3241bdb 100644
--- a/src/convex_optimization.py
+++ b/src/convex_optimization.py
@@ -36,8 +36,7 @@ def sparse_recovery(M_val, w_val, lbda):
     w = theano.shared(w_val.astype(theano.config.floatX))
     lbda = theano.shared(lbda.astype(theano.config.floatX))
 
-    #Objective
-    y =  lbda * (theta_).norm(1) - 1./m*(
+    y =  lbda*1./np.sqrt(m) * (theta_).norm(1) - 1./m*(
             tensor.dot(1-w, tensor.log(1-tensor.exp(M.dot(theta_ *1./(n*m)))))\
             + (1 - 1./(n*m)) * tensor.dot(1 - w, tensor.dot(M, theta_)) \
             + tensor.dot(w, tensor.dot(M, theta_)))  
@@ -102,8 +101,8 @@ def diff_and_opt(theta, theta_, M, M_val, w, lbda, y):
     h = cvxopt.matrix(0.0, (n,1))
 
     #Relaxing precision constraints
-    cvxopt.solvers.options['feastol'] = 2e-5
-    cvxopt.solvers.options['abstol'] = 2e-5
+    #cvxopt.solvers.options['feastol'] = 2e-5
+    #cvxopt.solvers.options['abstol'] = 2e-5
     #cvxopt.solvers.options['maxiters'] = 100
     cvxopt.solvers.options['show_progress'] = True
     try:
@@ -111,7 +110,9 @@ def diff_and_opt(theta, theta_, M, M_val, w, lbda, y):
     except ArithmeticError:
         print("ArithmeticError thrown, change initial point"+\
               " given to the solver")
-    except 
+
+    if cvxopt.solvers.options['show_progress']:
+        print(1 - np.exp(theta))
 
     return 1 - np.exp(theta), theta
 
@@ -121,9 +122,9 @@ def test():
     unit test
     """
     lbda = 1
-    G = cascade_creation.InfluenceGraph(max_proba=.8)
-    G.erdos_init(n=100, p = .1)
-    A = cascade_creation.generate_cascades(G, .1, 100)
+    G = cascade_creation.InfluenceGraph(max_proba=.9)
+    G.erdos_init(n=10, p = .3)
+    A = cascade_creation.generate_cascades(G, .1, 1000)
     M_val, w_val = cascade_creation.icc_matrixvector_for_node(A, 0)
     
     #Type lasso
@@ -134,6 +135,7 @@ def test():
     #Sparse recovery
     if 1:
         p_vec, theta = sparse_recovery(M_val, w_val, lbda)
+        print(G.mat[0])
         print(p_vec)
 
 if __name__=="__main__":
diff --git a/src/make_plots.py b/src/make_plots.py
index 57c5caa..60c2292 100644
--- a/src/make_plots.py
+++ b/src/make_plots.py
@@ -32,11 +32,12 @@ def test():
     unit test
     """
     G = cascade_creation.InfluenceGraph(max_proba=.8)
-    G.erdos_init(n=100, p=.1)
-    A = cascade_creation.generate_cascades(G, p_init=.02, n_cascades=1000)
+    G.erdos_init(n=20, p=.2)
+    print(G.mat)
+    A = cascade_creation.generate_cascades(G, p_init=.1, n_cascades=1000)
     G_hat = algorithms.recovery_l1obj_l2constraint(G, A,
                 passed_function=convex_optimization.sparse_recovery,
-                floor_cstt=.1, lbda=1)
+                floor_cstt=.1, lbda=20)
     algorithms.correctness_measure(G, G_hat, print_values=True)
 
 if __name__=="__main__":