Digging into the data

1 files changed, 69 insertions, 14 deletions

diff --git a/hawkes/cause.py b/hawkes/cause.py
index 0cce9d2..fddbfa9 100644
--- a/hawkes/cause.py
+++ b/hawkes/cause.py
@@ -1,24 +1,79 @@
 from cPickle import load
-from math import exp
+from math import exp, sin
+from csv import reader
+from data2 import parse
+import sys
+import networkx as nx
+import matplotlib.pyplot as plt
 
 
-def main(a):
-    lamb, alpha, mu = a
-    dr = 0
-    r = 0
+def fatal():
+    with open(sys.argv[1]) as fh:
+        fh.readline()
+        r = reader(fh)
+        d = {i + 1: parse(row[7]) for (i, row) in enumerate(r)}
+    d = {k: v for k, v in d.iteritems() if v}
+    return d
+
+
+def main(lamb, alpha, mu):
+    G = nx.DiGraph()
+    r, dr, i = 0, 0, 0
+    drf, iff, rf = 0, 0, 0
+    dnf, rnf, inf = 0, 0, 0
+    si = 0
+    f = fatal().items()
+    l = []
     for ((n1, t1), s) in event_edges.iteritems():
+        G.add_node((n1, t1))
         if not s:
             dr += 1
-        if lamb > sum(alpha * w * mu * exp(-mu * (t1 - t2))
-                      for (n2, t2, w) in s):
+            if (n1, t1) in f:
+                drf += 1
+            else:
+                dnf += 1
+            continue
+        br = lamb * (1 + 0.43 * sin(0.0172 * t1 + 4.36))
+        prl = sorted([(n2, t2, alpha / d * mu * exp(-mu * (t1 - t2)))
+                      for (n2, t2, d) in s], reverse=True)
+        pr = sum(e[2] for e in prl)
+        #if sum(e[2] for e in prl[:1]) > br:
+        #    G.add_edge((n1, t1), tuple(prl[0][:2]))
+        if br > pr:
             r += 1
-    return lamb, alpha, mu, dr, r
+            if (n1, t1) in f:
+                rf += 1
+            else:
+                rnf += 1
+        else:
+            G.add_edge((n1, t1), tuple(prl[0][:2]))
+            l.append(prl[0][2] / br)
+            i += 1
+            if (n1, t1) in f:
+                iff += 1
+            else:
+                inf += 1
+    print "nedges:", G.number_of_edges()
+    cs = {}
+    for c in nx.weakly_connected_components(G):
+        cs[len(c)] = cs.get(len(c), 0) + 1
+    cs = sorted(cs.iteritems(), key=lambda x: x[0])
+    x, y = zip(*cs)
+    print cs
+    plt.loglog(x, y, "-")
+    plt.xlabel("Cascade size")
+    plt.ylabel("Number of cascades")
+    plt.savefig("dist.pdf")
+    l.sort(reverse=True)
+    plt.plot(l)
+    plt.show()
+    return (lamb, alpha, mu, dr, r, i, drf, rf, iff,
+            dnf, rnf, inf, si, len(event_edges))
 
 
 if __name__ == "__main__":
-    nodes, edges, events, event_edges = load(open("data.pickle", "rb"))
-    for i, line in enumerate(open("values-sorted.txt")):
-        if i > 100:
-            break
-        lamb, alpha, mu, v = map(float, line.strip().split())
-        print main((lamb, alpha, mu))
+    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())
+    print main(lamb, alpha, mu)