Print some more stats about the causes

1 files changed, 36 insertions, 21 deletions

diff --git a/hawkes_experiments/cause.py b/hawkes_experiments/cause.py
index 17ec884..800f699 100644
--- a/hawkes_experiments/cause.py
+++ b/hawkes_experiments/cause.py
@@ -2,10 +2,9 @@ from cPickle import load
 from math import exp, sin
 from collections import Counter
 from csv import reader, writer
-from data2 import parse
+from data import parse
 import sys
 import networkx as nx
-import matplotlib.pyplot as plt
 import numpy as np
 
 
@@ -31,42 +30,58 @@ def cause(lamb, alpha, mu):
                 fatal_droots += 1
             continue
         background_rate = lamb * (1 + 0.43 * sin(0.0172 * t1 + 4.36))
-        neighbors = sorted([(n2, t2, alpha / d * mu * exp(-mu * (t1 - t2)))
-                            for (n2, t2, d) in s], reverse=True)
-        neighbor_rate = sum(e[2] for e in neighbors)
+        parents = sorted([(n2, t2, alpha / d * mu * exp(-mu * (t1 - t2)), d)
+                          for (n2, t2, d) in s], reverse=True,
+                         key=lambda x: x[2])
+        parent_rate = sum(e[2] for e in parents)
         # if sum(e[2] for e in prl[:1]) > br:
         #    G.add_edge((n1, t1), tuple(prl[0][:2]))
-        if background_rate > neighbor_rate:
+        if background_rate > parent_rate:
             roots += 1
             if (n1, t1) in fatals:
                 fatal_roots += 1
         else:
-            G.add_edge((n1, t1), tuple(neighbors[0][:2]))
+            G.add_edge(tuple(parents[0][:2]), (n1, t1),
+                       weight=parents[0][3])
             # l.append(prl[0][2] / br)
             infections += 1
             if (n1, t1) in fatals:
                 fatal_infections += 1
-    # l.sort(reverse=True)
-    # plt.plot(l)
-    # plt.show()
     return (droots, roots, infections, fatal_droots,
             fatal_roots, fatal_infections, G)
 
 
 def analyze_graph(G):
-    counts = Counter(len(c) for c in nx.weakly_connected_components(G))
-    w = writer(open("components_dist.csv", "w"))
-    w.writerows(counts.most_common())
-    edges = ((n1, t1, n2, t2) for ((n1, t1), (n2, t2)) in G.edges_iter())
-    e = writer(open("edges.csv", "w"))
-    e.writerows(edges)
+    csizes = [len(c) for c in nx.weakly_connected_components(G)]
+    print "cascades: {0}, min: {1}, max: {2}, mean: {3}, median: {4}".format(
+        len(csizes),  np.min(csizes), np.max(csizes), np.mean(csizes),
+        np.median(csizes))
+    # counts = Counter(l)
+    # w = writer(open("components_dist.csv", "w"))
+    # w.writerows(counts.most_common())
+    edges = list(G.edges_iter(data=True))
+    print "edges: {0}".format(len(edges))
+    times = [e[1][1] - e[0][1] for e in edges]
+    distances = [e[2]["weight"] for e in edges]
+    print "times, min: {0}, max: {1}, mean: {2}, median: {3}".format(
+        np.min(times), np.max(times), np.mean(times), np.median(times))
+    print "distances, min: {0}, max: {1}, mean: {2}, median: {3}".format(
+        np.min(distances), np.max(distances), np.mean(distances),
+        np.median(distances))
+    # e = writer(open("edges.csv", "w"))
+    # e.writerows(edges)
 
 
 if __name__ == "__main__":
-    nodes, edges, events, event_edges = load(open("data2.pickle", "rb"))
-    lamb, alpha, mu = 1.1847510744e-05, 0.00316718040144, 0.00393069204339
-    # print len(event_edges), sum(len(e) for e in events.itervalues())
-    # print len(fatal())
-    (doors, roots, infections, fatal_droots,
+    nodes, edges, events, event_edges = load(open("data-dist1.pickle", "rb"))
+    lamb, alpha, mu = 1.86602117779e-05, 0.0433473674726, 0.00109325510695
+    # lamb, alpha, mu = 1.87717287808e-05, 5.12006113875e+14, 4.20918377797e-20
+    (droots, roots, infections, fatal_droots,
      fatal_roots, fatal_infections, G) = cause(lamb, alpha, mu)
+    r = "events: {0}, droots: {1}, roots: {2}, infections: {3}, "\
+        "fatal_droots: {4}, fatal_roots: {5}, "\
+        "fatal_infections: {6}".format(len(event_edges), droots, roots,
+                                       infections, fatal_droots, fatal_roots,
+                                       fatal_infections)
+    print r
     analyze_graph(G)