All in one if no root hubs, singletons if no cluster matches.

code/absinth_nx.py

@@ -9,7 +9,7 @@ import numpy as np # for calculations
 nlp = spacy.load('en') # standard english nlp
 
 
-def frequencies(corpus_path, target, stop_words=['utc', 'new', 'other'], allowed_tags=['NN','NNS','JJ','JJS','JJR','NNP'], min_context_size = 4, max_nodes=10000, max_edges=1000000):
+def frequencies(corpus_path, target, stop_words=['utc', 'new', 'other'], allowed_tags=['NN','NNS','JJ','JJS','JJR','NNP'], min_context_size = 4, max_nodes=100000, max_edges=10000000):
     
     node_freq = dict()
     edge_freq = dict()
@@ -201,14 +201,12 @@ def disambiguate(mst, hubs, contexts):
         
         try:
             if max(vector) == 0:
-                result.append((backup_cluster, idx))
-                backup_cluster += 1
+                pass
             else:    
                 cluster = np.argmax(vector)
                 result.append((cluster, idx))
         except:
-            result.append((backup_cluster, idx))
-            backup_cluster += 1
+            result.append((0, idx))
         
     return result