🎨 documentation

cdlib/lifecycles/algorithms/event_analysis.py

@@ -26,9 +26,9 @@ def _analyze_one_struct(target, reference) -> dict:
     # ids_for_entropy.extend(newels_ids)
 
     return {
-        "U": facet_unicity(ids_for_entropy),
-        "I": facet_identity(target, reference),
-        "O": facet_outflow(target, reference),
+        "Unicity": facet_unicity(ids_for_entropy),
+        "Identity": facet_identity(target, reference),
+        "Outflow": facet_outflow(target, reference),
         "size": len(target),
     }
 
@@ -69,14 +69,14 @@ def event_weights_from_flow(analyzed_flows: dict, direction: str) -> dict:
 
 def _compute_event_scores(analyzed_flow: dict) -> list:
     return [
-        (analyzed_flow["U"]) * (1 - analyzed_flow["I"]) * analyzed_flow["O"],
-        (1 - analyzed_flow["U"]) * (1 - analyzed_flow["I"]) * analyzed_flow["O"],
-        (analyzed_flow["U"]) * analyzed_flow["I"] * analyzed_flow["O"],
-        (1 - analyzed_flow["U"]) * analyzed_flow["I"] * analyzed_flow["O"],
-        (analyzed_flow["U"]) * analyzed_flow["I"] * (1 - analyzed_flow["O"]),
-        (1 - analyzed_flow["U"]) * analyzed_flow["I"] * (1 - analyzed_flow["O"]),
-        (analyzed_flow["U"]) * (1 - analyzed_flow["I"]) * (1 - analyzed_flow["O"]),
-        (1 - analyzed_flow["U"]) * (1 - analyzed_flow["I"]) * (1 - analyzed_flow["O"]),
+        (analyzed_flow["Unicity"]) * (1 - analyzed_flow["Identity"]) * analyzed_flow["Outflow"],
+        (1 - analyzed_flow["Unicity"]) * (1 - analyzed_flow["Identity"]) * analyzed_flow["Outflow"],
+        (analyzed_flow["Unicity"]) * analyzed_flow["Identity"] * analyzed_flow["Outflow"],
+        (1 - analyzed_flow["Unicity"]) * analyzed_flow["Identity"] * analyzed_flow["Outflow"],
+        (analyzed_flow["Unicity"]) * analyzed_flow["Identity"] * (1 - analyzed_flow["Outflow"]),
+        (1 - analyzed_flow["Unicity"]) * analyzed_flow["Identity"] * (1 - analyzed_flow["Outflow"]),
+        (analyzed_flow["Unicity"]) * (1 - analyzed_flow["Identity"]) * (1 - analyzed_flow["Outflow"]),
+        (1 - analyzed_flow["Unicity"]) * (1 - analyzed_flow["Identity"]) * (1 - analyzed_flow["Outflow"]),
     ]
 
 

cdlib/lifecycles/classes/event.py

@@ -190,8 +190,8 @@ def compute_events_from_explicit_matching(self):
         }
 
         for e in lifecycle:
-            xtid = e[0].split("_")[0]
-            ytid = e[1].split("_")[0]
+            xtid = int(e[0].split("_")[0])
+            ytid = int(e[1].split("_")[0])
             if xtid < ytid:
                 flows["+"][e[0]][e[1]] = set(
                     self.clustering.get_community(e[0])
@@ -288,8 +288,8 @@ def compute_events_with_custom_matching(
         }
 
         for e in lifecycle:
-            xtid = e[0].split("_")[0]
-            ytid = e[1].split("_")[0]
+            xtid = int(e[0].split("_")[0])
+            ytid = int(e[1].split("_")[0])
             if e[2] > threshold:
                 if xtid < ytid:
                     flows["+"][e[0]][e[1]] = set(
@@ -321,7 +321,6 @@ def __instantiate_events(self, flows, events):
             self.events[cid].set_out_flow(flows["+"][cid])
 
         for cid in flows["-"]:
-            print(cid)
             if cid not in self.events:
                 self.events[cid] = CommunityEvent(cid)
             self.events[cid].set_in_flow(flows["-"][cid])

cdlib/test/test_events.py

@@ -6,6 +6,7 @@
 import networkx as nx
 from networkx.generators.community import LFR_benchmark_graph
 import matplotlib.pyplot as plt
+import dynetx as dn
 import os
 from cdlib.viz import (
     plot_flow,
@@ -78,6 +79,10 @@ def test_custom_matching(self):
         c = events.analyze_flows("+")
         self.assertIsInstance(c, dict)
 
+        events.compute_events_with_custom_matching(jaccard, two_sided=False, threshold=0)
+        c = events.analyze_flows("+")
+        self.assertIsInstance(c, dict)
+
     def test_polytree(self):
         tc = TemporalClustering()
         for t in range(0, 10):
@@ -155,6 +160,20 @@ def test_viz(self):
         plt.savefig("td.pdf")
         os.remove("td.pdf")
 
+    def test_explicit(self):
+
+        dg = dn.DynGraph()
+        for x in range(10):
+            g = nx.erdos_renyi_graph(200, 0.05)
+            dg.add_interactions_from(list(g.edges()), t=x)
+        coms = algorithms.tiles(dg, 2)
+
+        events = LifeCycle(coms)
+        events.compute_events_from_explicit_matching()
+
+        c = events.analyze_flows("+")
+        self.assertIsInstance(c, dict)
+
     def test_node_attributes(self):
         import random
 

docs/reference/events.rst

@@ -38,8 +38,7 @@ To analyze such pre-computed events apply the following snippet:
         dg.add_interactions_from(list(g.edges()), t=x)
     coms = algorithms.tiles(dg, 2)
 
-    lc = LifeCycle()
-    lc.from_temporal_clustering(coms)
+    lc = LifeCycle(coms)
     lc.compute_events_from_explicit_matching()