correctness measure

1 files changed, 20 insertions, 3 deletions

diff --git a/jpa_test/algorithms.py b/jpa_test/algorithms.py
index 7cdf093..2a32f57 100644
--- a/jpa_test/algorithms.py
+++ b/jpa_test/algorithms.py
@@ -30,6 +30,19 @@ def greedy_prediction(G, cascades):
                 if (cascade.infection_time(parent) == \
                         [item - 1 for item in cascade.infection_time(node)]):
                     unaccounted[t] = False
+    return G_hat
+
+
+def correctness_measure(G, G_hat):
+    """
+    Measures correctness of estimated graph G_hat to ground truth G
+    """
+    edges = set(G.edges())
+    edges_hat = set(G_hat.edges())
+    fp = edges_hat - edges
+    fn = edges - edges_hat
+    gp = edges | edges_hat
+    return fp, fn, gp
 
 
 def test():
@@ -37,11 +50,15 @@ def test():
     unit test
     """
     G = cascade_creation.InfluenceGraph(max_proba = .3)
-    G.erdos_init(n = 100, p = 1)
+    G.erdos_init(n = 100, p = .1)
     import time
     t0 = time.time()
-    A = cascade_creation.generate_cascades(G, .1, 4)
-    greedy_prediction(G, A)
+    A = cascade_creation.generate_cascades(G, .1, 100)
+    G_hat = greedy_prediction(G, A)
+    fp, fn, gp = correctness_measure(G, G_hat)
+    print "False Positive: {}".format(len(fp))
+    print "False Negative: {}".format(len(fn))
+    print "Good Positives: {}".format(len(gp))
     t1 = time.time()
     print t1 - t0