fixes

src/data_helpers.py

+import numpy as np
 import re
 from operator import itemgetter
 import bs4
@@ -108,7 +109,8 @@ class HasDescriptionNode(dict):
         vecs=[]
         ncs = []
         doc = nlp(self.d["text"])
-        
+        if not list(doc.noun_chunks):
+            doc = nlp("This is a dummy document.")
         def not_valid(n,d):
             if n.text in ["thence","they","them","her","him","it"]:
                 return True
@@ -132,6 +134,11 @@ class HasDescriptionNode(dict):
                 continue
             vecs.append(nc.vector/nc.vector_norm)
             ncs.append(nc)
+        #print(vecs[0].shape)
+        """
+        if not ncs:
+            return [np.zeros(96)],["empty"]
+        """
         """
         for nc in [t for t in doc if t.pos_ == "VERB"]:
             vecs.append(nc.vector/nc.vector_norm)
@@ -146,6 +153,8 @@ class HasDescriptionNode(dict):
         """
         vecs,ncs = self.get_noun_chunk_vectors()
         newcopy = HasDescriptionNode(None,"None")
+        if not classifier:
+            return newcopy
         if not ncs:
             return newcopy
         maxlen=max([len(x) for x in ncs])

src/graph_helpers.py

@@ -37,7 +37,7 @@ def simplify_text_description_nodes(G,node_index_dict,mode="None",min_freq=1):
     trialnodes=[n for n in G.nodes(data=True) if isinstance(n[1]["nodeobj"],dh.TrialNode)]
     descr_nodes=[]
     mask=[]
-    
+    related_cat2=[]
     # we iterate over all trials
     for i,tn in enumerate(trialnodes):
         #get corresponding cat node
@@ -58,6 +58,7 @@ def simplify_text_description_nodes(G,node_index_dict,mode="None",min_freq=1):
                     descr_nodes.append(G.nodes[nb]["nodeobj"])
                     tid=(tn[0],nb)  
                     Xid.append(tid)
+                    related_cat2.append(category)
                     for dv in descr_vectors:
                         #put noun chunk vector into training data
                         Xvector.append(dv)
@@ -67,8 +68,7 @@ def simplify_text_description_nodes(G,node_index_dict,mode="None",min_freq=1):
     if mode=="classifier":
         #fit a classifier to learn a mapping between noun chunks and labels
         clf=LogisticRegression()
-        clf.fit(Xvector,related_cat)
-    
+        clf.fit(Xvector,related_cat) 
     
     # now we can remove the textdescription nodes and insert their simplified fporms
     for i,idx in enumerate(Xid):
@@ -78,7 +78,11 @@ def simplify_text_description_nodes(G,node_index_dict,mode="None",min_freq=1):
             #node_index_dict.pop(descr_nodes[i])
 
         if mode == "classifier":
-            simp_descr=descr_nodes[i].simplify(clf,list(clf.classes_).index(related_cat[i]))
+            #print(clf.classes_,related_cat2[i],"damage" in related_cat,"damage" in related_cat2)
+            if related_cat2[i] in clf.classes_:
+                simp_descr=descr_nodes[i].simplify(clf,list(clf.classes_).index(related_cat2[i]))
+            else:
+                simp_descr=descr_nodes[i].simplify(None,None)
         elif mode == "spacy_direct_object":
             simp_descr=descr_nodes[i].simplify_to_direct_object()